{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-05-29 10:01:03,253:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:01:03,268:INFO: - done.\n",
      "\n",
      "2018-05-29 10:02:01,739:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:02:01,739:INFO: - done.\n",
      "\n",
      "2018-05-29 10:02:03,898:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:16:59,556:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:16:59,557:INFO: - done.\n",
      "\n",
      "2018-05-29 10:17:08,963:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:17:08,964:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:17:27,793:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:17:27,793:INFO: - done.\n",
      "\n",
      "2018-05-29 10:17:37,918:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:17:37,919:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:27:11,208:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:27:11,208:INFO: - done.\n",
      "\n",
      "2018-05-29 10:27:21,562:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:27:21,562:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:28:34,649:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:28:34,650:INFO: - done.\n",
      "\n",
      "2018-05-29 10:28:44,816:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:28:44,816:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:29:41,946:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:29:41,946:INFO: - done.\n",
      "\n",
      "2018-05-29 10:29:52,025:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:29:52,026:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:30:17,230:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:30:17,231:INFO: - done.\n",
      "\n",
      "2018-05-29 10:30:24,487:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:30:24,487:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:30:50,395:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:30:50,396:INFO: - done.\n",
      "\n",
      "2018-05-29 10:30:57,614:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:30:57,614:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:31:30,774:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:31:30,774:INFO: - done.\n",
      "\n",
      "2018-05-29 10:31:38,197:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:31:38,198:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:31:52,005:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:31:52,005:INFO: - done.\n",
      "\n",
      "2018-05-29 10:31:59,392:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:31:59,392:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:32:49,476:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:32:49,477:INFO: - done.\n",
      "\n",
      "2018-05-29 10:32:56,724:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:32:56,724:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:33:19,871:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:33:19,871:INFO: - done.\n",
      "\n",
      "2018-05-29 10:33:27,094:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:33:27,094:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:34:20,350:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:34:20,351:INFO: - done.\n",
      "\n",
      "2018-05-29 10:34:27,587:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:34:27,587:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:42:12,105:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:42:12,106:INFO: - done.\n",
      "\n",
      "2018-05-29 10:42:31,220:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:42:31,221:INFO: - done.\n",
      "\n",
      "2018-05-29 10:42:38,533:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:42:38,533:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:43:10,157:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:43:10,157:INFO: - done.\n",
      "\n",
      "2018-05-29 10:43:17,638:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:43:17,638:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:43:35,035:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:43:35,036:INFO: - done.\n",
      "\n",
      "2018-05-29 10:43:42,315:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:43:42,315:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:46:33,314:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:46:33,315:INFO: - done.\n",
      "\n",
      "2018-05-29 10:46:40,636:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:46:40,636:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:46:55,208:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:46:55,209:INFO: - done.\n",
      "\n",
      "2018-05-29 10:47:02,695:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:47:02,696:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:47:03,649:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 10:47:20,406:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:47:20,407:INFO: - done.\n",
      "\n",
      "2018-05-29 10:47:27,656:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:47:27,657:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:47:28,583:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 10:47:48,546:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:47:48,547:INFO: - done.\n",
      "\n",
      "2018-05-29 10:47:55,791:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:47:55,791:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:47:56,751:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 10:47:56,754:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 10:47:57,741:INFO: - Train metrics: PSNR: 13.470 ; SSIM: 0.588 ; g_loss: 0.050 ; d_loss: 0.960\n",
      "\n",
      "2018-05-29 10:47:57,742:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 10:47:58,752:INFO: - Train metrics: PSNR: 15.188 ; SSIM: 0.575 ; g_loss: 0.035 ; d_loss: 0.895\n",
      "\n",
      "2018-05-29 10:47:58,753:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-29 10:47:59,739:INFO: - Train metrics: PSNR: 15.863 ; SSIM: 0.552 ; g_loss: 0.032 ; d_loss: 0.903\n",
      "\n",
      "2018-05-29 10:47:59,740:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-29 10:48:00,728:INFO: - Train metrics: PSNR: 17.320 ; SSIM: 0.638 ; g_loss: 0.023 ; d_loss: 0.835\n",
      "\n",
      "2018-05-29 10:48:00,729:INFO: Epoch 6/100\n",
      "\n",
      "2018-05-29 10:48:01,710:INFO: - Train metrics: PSNR: 17.140 ; SSIM: 0.657 ; g_loss: 0.024 ; d_loss: 0.986\n",
      "\n",
      "2018-05-29 10:48:01,711:INFO: Epoch 7/100\n",
      "\n",
      "2018-05-29 10:48:02,700:INFO: - Train metrics: PSNR: 17.096 ; SSIM: 0.695 ; g_loss: 0.023 ; d_loss: 0.882\n",
      "\n",
      "2018-05-29 10:48:02,701:INFO: Epoch 8/100\n",
      "\n",
      "2018-05-29 10:48:03,685:INFO: - Train metrics: PSNR: 17.615 ; SSIM: 0.700 ; g_loss: 0.022 ; d_loss: 0.862\n",
      "\n",
      "2018-05-29 10:48:03,686:INFO: Epoch 9/100\n",
      "\n",
      "2018-05-29 10:48:04,663:INFO: - Train metrics: PSNR: 18.245 ; SSIM: 0.720 ; g_loss: 0.019 ; d_loss: 0.898\n",
      "\n",
      "2018-05-29 10:48:04,664:INFO: Epoch 10/100\n",
      "\n",
      "2018-05-29 10:48:05,647:INFO: - Train metrics: PSNR: 18.254 ; SSIM: 0.734 ; g_loss: 0.019 ; d_loss: 0.902\n",
      "\n",
      "2018-05-29 10:48:05,648:INFO: Epoch 11/100\n",
      "\n",
      "2018-05-29 10:48:06,622:INFO: - Train metrics: PSNR: 19.059 ; SSIM: 0.767 ; g_loss: 0.016 ; d_loss: 0.771\n",
      "\n",
      "2018-05-29 10:48:06,623:INFO: Epoch 12/100\n",
      "\n",
      "2018-05-29 10:48:07,604:INFO: - Train metrics: PSNR: 19.076 ; SSIM: 0.753 ; g_loss: 0.016 ; d_loss: 0.698\n",
      "\n",
      "2018-05-29 10:48:07,604:INFO: Epoch 13/100\n",
      "\n",
      "2018-05-29 10:48:08,578:INFO: - Train metrics: PSNR: 19.636 ; SSIM: 0.770 ; g_loss: 0.015 ; d_loss: 0.611\n",
      "\n",
      "2018-05-29 10:48:08,579:INFO: Epoch 14/100\n",
      "\n",
      "2018-05-29 10:48:09,554:INFO: - Train metrics: PSNR: 19.731 ; SSIM: 0.808 ; g_loss: 0.015 ; d_loss: 0.828\n",
      "\n",
      "2018-05-29 10:48:09,555:INFO: Epoch 15/100\n",
      "\n",
      "2018-05-29 10:48:10,522:INFO: - Train metrics: PSNR: 19.857 ; SSIM: 0.825 ; g_loss: 0.014 ; d_loss: 0.524\n",
      "\n",
      "2018-05-29 10:48:10,523:INFO: Epoch 16/100\n",
      "\n",
      "2018-05-29 10:48:11,489:INFO: - Train metrics: PSNR: 20.015 ; SSIM: 0.821 ; g_loss: 0.014 ; d_loss: 0.510\n",
      "\n",
      "2018-05-29 10:48:11,490:INFO: Epoch 17/100\n",
      "\n",
      "2018-05-29 10:48:12,465:INFO: - Train metrics: PSNR: 20.179 ; SSIM: 0.830 ; g_loss: 0.013 ; d_loss: 0.944\n",
      "\n",
      "2018-05-29 10:48:12,466:INFO: Epoch 18/100\n",
      "\n",
      "2018-05-29 10:51:18,127:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:51:18,127:INFO: - done.\n",
      "\n",
      "2018-05-29 10:51:25,392:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:51:25,392:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:51:26,351:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 10:51:26,354:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 10:51:27,351:INFO: - Train metrics: PSNR: 13.468 ; SSIM: 0.588 ; g_loss: 0.050 ; d_loss: 0.960\n",
      "\n",
      "2018-05-29 10:51:27,352:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 10:51:28,345:INFO: - Train metrics: PSNR: 15.188 ; SSIM: 0.575 ; g_loss: 0.035 ; d_loss: 0.895\n",
      "\n",
      "2018-05-29 10:51:28,346:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-29 10:51:29,343:INFO: - Train metrics: PSNR: 15.862 ; SSIM: 0.552 ; g_loss: 0.032 ; d_loss: 0.905\n",
      "\n",
      "2018-05-29 10:51:29,343:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-29 10:51:30,337:INFO: - Train metrics: PSNR: 17.320 ; SSIM: 0.638 ; g_loss: 0.023 ; d_loss: 0.833\n",
      "\n",
      "2018-05-29 10:51:30,338:INFO: Epoch 6/100\n",
      "\n",
      "2018-05-29 10:51:31,334:INFO: - Train metrics: PSNR: 17.129 ; SSIM: 0.657 ; g_loss: 0.024 ; d_loss: 0.993\n",
      "\n",
      "2018-05-29 10:51:31,335:INFO: Epoch 7/100\n",
      "\n",
      "2018-05-29 10:51:32,337:INFO: - Train metrics: PSNR: 17.099 ; SSIM: 0.695 ; g_loss: 0.023 ; d_loss: 0.908\n",
      "\n",
      "2018-05-29 10:51:32,338:INFO: Epoch 8/100\n",
      "\n",
      "2018-05-29 10:51:33,332:INFO: - Train metrics: PSNR: 17.632 ; SSIM: 0.701 ; g_loss: 0.021 ; d_loss: 0.828\n",
      "\n",
      "2018-05-29 10:51:33,333:INFO: Epoch 9/100\n",
      "\n",
      "2018-05-29 10:51:34,327:INFO: - Train metrics: PSNR: 18.249 ; SSIM: 0.721 ; g_loss: 0.019 ; d_loss: 0.872\n",
      "\n",
      "2018-05-29 10:51:34,328:INFO: Epoch 10/100\n",
      "\n",
      "2018-05-29 10:51:35,246:INFO: - Train metrics: PSNR: 18.269 ; SSIM: 0.735 ; g_loss: 0.019 ; d_loss: 0.760\n",
      "\n",
      "2018-05-29 10:51:35,247:INFO: Epoch 11/100\n",
      "\n",
      "2018-05-29 10:51:36,228:INFO: - Train metrics: PSNR: 19.043 ; SSIM: 0.767 ; g_loss: 0.016 ; d_loss: 0.749\n",
      "\n",
      "2018-05-29 10:51:36,229:INFO: Epoch 12/100\n",
      "\n",
      "2018-05-29 10:51:37,213:INFO: - Train metrics: PSNR: 19.075 ; SSIM: 0.753 ; g_loss: 0.016 ; d_loss: 0.729\n",
      "\n",
      "2018-05-29 10:51:37,214:INFO: Epoch 13/100\n",
      "\n",
      "2018-05-29 10:51:38,213:INFO: - Train metrics: PSNR: 19.655 ; SSIM: 0.770 ; g_loss: 0.015 ; d_loss: 0.566\n",
      "\n",
      "2018-05-29 10:51:38,214:INFO: Epoch 14/100\n",
      "\n",
      "2018-05-29 10:51:39,201:INFO: - Train metrics: PSNR: 19.732 ; SSIM: 0.809 ; g_loss: 0.015 ; d_loss: 0.659\n",
      "\n",
      "2018-05-29 10:51:39,202:INFO: Epoch 15/100\n",
      "\n",
      "2018-05-29 10:51:40,207:INFO: - Train metrics: PSNR: 19.857 ; SSIM: 0.824 ; g_loss: 0.014 ; d_loss: 0.472\n",
      "\n",
      "2018-05-29 10:51:40,208:INFO: Epoch 16/100\n",
      "\n",
      "2018-05-29 10:51:41,208:INFO: - Train metrics: PSNR: 20.014 ; SSIM: 0.821 ; g_loss: 0.014 ; d_loss: 0.791\n",
      "\n",
      "2018-05-29 10:51:41,209:INFO: Epoch 17/100\n",
      "\n",
      "2018-05-29 10:51:42,198:INFO: - Train metrics: PSNR: 20.184 ; SSIM: 0.830 ; g_loss: 0.013 ; d_loss: 0.846\n",
      "\n",
      "2018-05-29 10:51:42,198:INFO: Epoch 18/100\n",
      "\n",
      "2018-05-29 10:51:43,186:INFO: - Train metrics: PSNR: 20.530 ; SSIM: 0.842 ; g_loss: 0.013 ; d_loss: 0.501\n",
      "\n",
      "2018-05-29 10:51:43,187:INFO: Epoch 19/100\n",
      "\n",
      "2018-05-29 10:51:44,088:INFO: - Train metrics: PSNR: 20.434 ; SSIM: 0.847 ; g_loss: 0.013 ; d_loss: 0.269\n",
      "\n",
      "2018-05-29 10:51:44,089:INFO: Epoch 20/100\n",
      "\n",
      "2018-05-29 10:51:44,980:INFO: - Train metrics: PSNR: 20.663 ; SSIM: 0.850 ; g_loss: 0.012 ; d_loss: 0.700\n",
      "\n",
      "2018-05-29 10:51:44,981:INFO: Epoch 21/100\n",
      "\n",
      "2018-05-29 10:51:45,970:INFO: - Train metrics: PSNR: 20.843 ; SSIM: 0.854 ; g_loss: 0.012 ; d_loss: 0.642\n",
      "\n",
      "2018-05-29 10:51:45,971:INFO: Epoch 22/100\n",
      "\n",
      "2018-05-29 10:51:46,956:INFO: - Train metrics: PSNR: 21.124 ; SSIM: 0.860 ; g_loss: 0.012 ; d_loss: 0.293\n",
      "\n",
      "2018-05-29 10:51:46,958:INFO: Epoch 23/100\n",
      "\n",
      "2018-05-29 10:51:47,935:INFO: - Train metrics: PSNR: 21.154 ; SSIM: 0.858 ; g_loss: 0.011 ; d_loss: 0.399\n",
      "\n",
      "2018-05-29 10:51:47,936:INFO: Epoch 24/100\n",
      "\n",
      "2018-05-29 10:51:48,922:INFO: - Train metrics: PSNR: 21.261 ; SSIM: 0.865 ; g_loss: 0.011 ; d_loss: 0.154\n",
      "\n",
      "2018-05-29 10:51:48,923:INFO: Epoch 25/100\n",
      "\n",
      "2018-05-29 10:51:49,914:INFO: - Train metrics: PSNR: 21.605 ; SSIM: 0.878 ; g_loss: 0.011 ; d_loss: 0.474\n",
      "\n",
      "2018-05-29 10:51:49,915:INFO: Epoch 26/100\n",
      "\n",
      "2018-05-29 10:51:53,789:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:51:53,790:INFO: - done.\n",
      "\n",
      "2018-05-29 10:52:01,100:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:52:01,100:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:52:02,048:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 10:52:02,051:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 10:52:02,999:INFO: - Train metrics: PSNR: 13.471 ; SSIM: 0.588 ; g_loss: 0.050 ; d_loss: 0.960\n",
      "\n",
      "2018-05-29 10:52:03,000:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 10:52:03,993:INFO: - Train metrics: PSNR: 15.187 ; SSIM: 0.575 ; g_loss: 0.035 ; d_loss: 0.895\n",
      "\n",
      "2018-05-29 10:52:03,993:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-29 10:52:04,898:INFO: - Train metrics: PSNR: 15.863 ; SSIM: 0.552 ; g_loss: 0.032 ; d_loss: 0.905\n",
      "\n",
      "2018-05-29 10:52:04,899:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-29 10:52:05,804:INFO: - Train metrics: PSNR: 17.319 ; SSIM: 0.639 ; g_loss: 0.023 ; d_loss: 0.831\n",
      "\n",
      "2018-05-29 10:52:05,805:INFO: Epoch 6/100\n",
      "\n",
      "2018-05-29 10:52:06,762:INFO: - Train metrics: PSNR: 17.138 ; SSIM: 0.657 ; g_loss: 0.024 ; d_loss: 0.992\n",
      "\n",
      "2018-05-29 10:52:06,763:INFO: Epoch 7/100\n",
      "\n",
      "2018-05-29 10:52:07,750:INFO: - Train metrics: PSNR: 17.105 ; SSIM: 0.695 ; g_loss: 0.023 ; d_loss: 0.844\n",
      "\n",
      "2018-05-29 10:52:07,751:INFO: Epoch 8/100\n",
      "\n",
      "2018-05-29 10:52:08,726:INFO: - Train metrics: PSNR: 17.633 ; SSIM: 0.701 ; g_loss: 0.021 ; d_loss: 0.873\n",
      "\n",
      "2018-05-29 10:52:08,727:INFO: Epoch 9/100\n",
      "\n",
      "2018-05-29 10:52:09,713:INFO: - Train metrics: PSNR: 18.261 ; SSIM: 0.720 ; g_loss: 0.019 ; d_loss: 0.713\n",
      "\n",
      "2018-05-29 10:52:09,713:INFO: Epoch 10/100\n",
      "\n",
      "2018-05-29 10:52:10,712:INFO: - Train metrics: PSNR: 18.270 ; SSIM: 0.736 ; g_loss: 0.019 ; d_loss: 0.784\n",
      "\n",
      "2018-05-29 10:52:10,712:INFO: Epoch 11/100\n",
      "\n",
      "2018-05-29 10:52:11,702:INFO: - Train metrics: PSNR: 19.065 ; SSIM: 0.767 ; g_loss: 0.016 ; d_loss: 0.743\n",
      "\n",
      "2018-05-29 10:52:11,703:INFO: Epoch 12/100\n",
      "\n",
      "2018-05-29 10:52:12,681:INFO: - Train metrics: PSNR: 19.081 ; SSIM: 0.752 ; g_loss: 0.016 ; d_loss: 0.582\n",
      "\n",
      "2018-05-29 10:52:12,682:INFO: Epoch 13/100\n",
      "\n",
      "2018-05-29 10:52:13,591:INFO: - Train metrics: PSNR: 19.653 ; SSIM: 0.770 ; g_loss: 0.014 ; d_loss: 0.924\n",
      "\n",
      "2018-05-29 10:52:13,592:INFO: Epoch 14/100\n",
      "\n",
      "2018-05-29 10:52:14,567:INFO: - Train metrics: PSNR: 19.734 ; SSIM: 0.809 ; g_loss: 0.015 ; d_loss: 0.588\n",
      "\n",
      "2018-05-29 10:52:14,568:INFO: Epoch 15/100\n",
      "\n",
      "2018-05-29 10:52:15,546:INFO: - Train metrics: PSNR: 19.861 ; SSIM: 0.824 ; g_loss: 0.014 ; d_loss: 0.656\n",
      "\n",
      "2018-05-29 10:52:15,547:INFO: Epoch 16/100\n",
      "\n",
      "2018-05-29 10:54:19,532:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:54:19,532:INFO: - done.\n",
      "\n",
      "2018-05-29 10:54:26,815:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:54:26,815:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:54:59,593:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 10:54:59,593:INFO: - done.\n",
      "\n",
      "2018-05-29 10:55:06,981:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 10:55:06,981:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 10:55:07,938:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 10:55:07,941:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 10:55:08,934:INFO: - Train metrics: PSNR: 13.467 ; SSIM: 0.588 ; g_loss: 0.050 ; d_loss: 0.960\n",
      "\n",
      "2018-05-29 10:55:08,935:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 10:55:09,929:INFO: - Train metrics: PSNR: 15.190 ; SSIM: 0.575 ; g_loss: 0.035 ; d_loss: 0.895\n",
      "\n",
      "2018-05-29 10:55:09,930:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-29 10:55:10,916:INFO: - Train metrics: PSNR: 15.863 ; SSIM: 0.552 ; g_loss: 0.032 ; d_loss: 0.906\n",
      "\n",
      "2018-05-29 10:55:10,917:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-29 10:55:11,908:INFO: - Train metrics: PSNR: 17.322 ; SSIM: 0.639 ; g_loss: 0.023 ; d_loss: 0.830\n",
      "\n",
      "2018-05-29 10:55:11,909:INFO: Epoch 6/100\n",
      "\n",
      "2018-05-29 10:55:12,892:INFO: - Train metrics: PSNR: 17.143 ; SSIM: 0.657 ; g_loss: 0.024 ; d_loss: 0.978\n",
      "\n",
      "2018-05-29 10:55:12,893:INFO: Epoch 7/100\n",
      "\n",
      "2018-05-29 10:55:13,900:INFO: - Train metrics: PSNR: 17.092 ; SSIM: 0.695 ; g_loss: 0.023 ; d_loss: 0.858\n",
      "\n",
      "2018-05-29 10:55:13,901:INFO: Epoch 8/100\n",
      "\n",
      "2018-05-29 10:55:14,901:INFO: - Train metrics: PSNR: 17.619 ; SSIM: 0.700 ; g_loss: 0.021 ; d_loss: 0.898\n",
      "\n",
      "2018-05-29 10:55:14,902:INFO: Epoch 9/100\n",
      "\n",
      "2018-05-29 10:55:15,906:INFO: - Train metrics: PSNR: 18.227 ; SSIM: 0.719 ; g_loss: 0.019 ; d_loss: 0.745\n",
      "\n",
      "2018-05-29 10:55:15,907:INFO: Epoch 10/100\n",
      "\n",
      "2018-05-29 10:55:16,903:INFO: - Train metrics: PSNR: 18.269 ; SSIM: 0.736 ; g_loss: 0.019 ; d_loss: 0.793\n",
      "\n",
      "2018-05-29 10:55:16,904:INFO: Epoch 11/100\n",
      "\n",
      "2018-05-29 10:55:17,906:INFO: - Train metrics: PSNR: 19.059 ; SSIM: 0.767 ; g_loss: 0.016 ; d_loss: 0.821\n",
      "\n",
      "2018-05-29 10:55:17,907:INFO: Epoch 12/100\n",
      "\n",
      "2018-05-29 10:55:18,893:INFO: - Train metrics: PSNR: 19.066 ; SSIM: 0.752 ; g_loss: 0.016 ; d_loss: 0.624\n",
      "\n",
      "2018-05-29 10:55:18,894:INFO: Epoch 13/100\n",
      "\n",
      "2018-05-29 10:55:19,886:INFO: - Train metrics: PSNR: 19.651 ; SSIM: 0.770 ; g_loss: 0.015 ; d_loss: 0.491\n",
      "\n",
      "2018-05-29 10:55:19,887:INFO: Epoch 14/100\n",
      "\n",
      "2018-05-29 10:55:20,873:INFO: - Train metrics: PSNR: 19.734 ; SSIM: 0.809 ; g_loss: 0.015 ; d_loss: 0.702\n",
      "\n",
      "2018-05-29 10:55:20,874:INFO: Epoch 15/100\n",
      "\n",
      "2018-05-29 11:00:28,209:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:00:28,209:INFO: - done.\n",
      "\n",
      "2018-05-29 11:00:35,467:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:00:35,467:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:00:36,411:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 11:05:43,976:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:05:43,976:INFO: - done.\n",
      "\n",
      "2018-05-29 11:05:51,347:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:05:51,347:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:05:52,293:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 11:06:04,194:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:06:04,194:INFO: - done.\n",
      "\n",
      "2018-05-29 11:06:11,483:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:06:11,484:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:06:12,445:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 11:06:13,224:INFO: - Eval metrics : PSNR: 11.341 ; SSIM: 0.508\n",
      "\n",
      "2018-05-29 11:06:58,197:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:06:58,197:INFO: - done.\n",
      "\n",
      "2018-05-29 11:07:05,486:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:07:05,486:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:07:06,438:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 11:07:07,203:INFO: - Eval metrics : PSNR: 11.341 ; SSIM: 0.508\n",
      "\n",
      "2018-05-29 11:07:16,911:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:07:16,911:INFO: - done.\n",
      "\n",
      "2018-05-29 11:07:24,501:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:07:24,501:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:07:25,435:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 11:07:26,202:INFO: - Eval metrics : PSNR: 11.341 ; SSIM: 0.508\n",
      "\n",
      "2018-05-29 11:08:00,614:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:08:00,614:INFO: - done.\n",
      "\n",
      "2018-05-29 11:08:07,941:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:08:07,941:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:08:08,893:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 11:08:09,586:INFO: - Eval metrics : PSNR: 11.341 ; SSIM: 0.508\n",
      "\n",
      "2018-05-29 11:08:09,586:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:09,587:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 11:08:10,581:INFO: - Train metrics: PSNR: 13.475 ; SSIM: 0.588 ; g_loss: 0.050 ; d_loss: 0.960\n",
      "\n",
      "2018-05-29 11:08:11,346:INFO: - Eval metrics : PSNR: 11.413 ; SSIM: 0.495\n",
      "\n",
      "2018-05-29 11:08:11,348:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:11,348:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 11:08:12,328:INFO: - Train metrics: PSNR: 15.185 ; SSIM: 0.575 ; g_loss: 0.035 ; d_loss: 0.895\n",
      "\n",
      "2018-05-29 11:08:13,100:INFO: - Eval metrics : PSNR: 11.556 ; SSIM: 0.491\n",
      "\n",
      "2018-05-29 11:08:13,100:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:13,101:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-29 11:08:14,081:INFO: - Train metrics: PSNR: 15.868 ; SSIM: 0.552 ; g_loss: 0.032 ; d_loss: 0.903\n",
      "\n",
      "2018-05-29 11:08:14,841:INFO: - Eval metrics : PSNR: 11.986 ; SSIM: 0.509\n",
      "\n",
      "2018-05-29 11:08:14,842:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:14,843:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-29 11:08:15,768:INFO: - Train metrics: PSNR: 17.317 ; SSIM: 0.638 ; g_loss: 0.023 ; d_loss: 0.834\n",
      "\n",
      "2018-05-29 11:08:16,452:INFO: - Eval metrics : PSNR: 12.146 ; SSIM: 0.524\n",
      "\n",
      "2018-05-29 11:08:16,453:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:16,454:INFO: Epoch 6/100\n",
      "\n",
      "2018-05-29 11:08:17,367:INFO: - Train metrics: PSNR: 17.135 ; SSIM: 0.656 ; g_loss: 0.024 ; d_loss: 0.984\n",
      "\n",
      "2018-05-29 11:08:18,139:INFO: - Eval metrics : PSNR: 12.159 ; SSIM: 0.520\n",
      "\n",
      "2018-05-29 11:08:18,140:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:18,141:INFO: Epoch 7/100\n",
      "\n",
      "2018-05-29 11:08:19,141:INFO: - Train metrics: PSNR: 17.086 ; SSIM: 0.695 ; g_loss: 0.023 ; d_loss: 0.844\n",
      "\n",
      "2018-05-29 11:08:19,904:INFO: - Eval metrics : PSNR: 12.772 ; SSIM: 0.509\n",
      "\n",
      "2018-05-29 11:08:19,905:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:19,906:INFO: Epoch 8/100\n",
      "\n",
      "2018-05-29 11:08:20,888:INFO: - Train metrics: PSNR: 17.624 ; SSIM: 0.700 ; g_loss: 0.021 ; d_loss: 0.854\n",
      "\n",
      "2018-05-29 11:08:21,653:INFO: - Eval metrics : PSNR: 13.698 ; SSIM: 0.537\n",
      "\n",
      "2018-05-29 11:08:21,654:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:21,655:INFO: Epoch 9/100\n",
      "\n",
      "2018-05-29 11:08:22,641:INFO: - Train metrics: PSNR: 18.234 ; SSIM: 0.719 ; g_loss: 0.019 ; d_loss: 0.733\n",
      "\n",
      "2018-05-29 11:08:23,424:INFO: - Eval metrics : PSNR: 14.069 ; SSIM: 0.532\n",
      "\n",
      "2018-05-29 11:08:23,425:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:23,425:INFO: Epoch 10/100\n",
      "\n",
      "2018-05-29 11:08:24,412:INFO: - Train metrics: PSNR: 18.269 ; SSIM: 0.736 ; g_loss: 0.019 ; d_loss: 0.837\n",
      "\n",
      "2018-05-29 11:08:25,187:INFO: - Eval metrics : PSNR: 13.893 ; SSIM: 0.508\n",
      "\n",
      "2018-05-29 11:08:25,188:INFO: Epoch 11/100\n",
      "\n",
      "2018-05-29 11:08:26,176:INFO: - Train metrics: PSNR: 19.050 ; SSIM: 0.767 ; g_loss: 0.016 ; d_loss: 0.799\n",
      "\n",
      "2018-05-29 11:08:26,943:INFO: - Eval metrics : PSNR: 14.701 ; SSIM: 0.520\n",
      "\n",
      "2018-05-29 11:08:26,944:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:26,945:INFO: Epoch 12/100\n",
      "\n",
      "2018-05-29 11:08:27,943:INFO: - Train metrics: PSNR: 19.090 ; SSIM: 0.753 ; g_loss: 0.016 ; d_loss: 0.638\n",
      "\n",
      "2018-05-29 11:08:28,707:INFO: - Eval metrics : PSNR: 15.873 ; SSIM: 0.558\n",
      "\n",
      "2018-05-29 11:08:28,708:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:28,709:INFO: Epoch 13/100\n",
      "\n",
      "2018-05-29 11:08:29,695:INFO: - Train metrics: PSNR: 19.647 ; SSIM: 0.771 ; g_loss: 0.015 ; d_loss: 0.498\n",
      "\n",
      "2018-05-29 11:08:30,456:INFO: - Eval metrics : PSNR: 16.622 ; SSIM: 0.585\n",
      "\n",
      "2018-05-29 11:08:30,457:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:30,458:INFO: Epoch 14/100\n",
      "\n",
      "2018-05-29 11:08:31,367:INFO: - Train metrics: PSNR: 19.752 ; SSIM: 0.810 ; g_loss: 0.014 ; d_loss: 0.857\n",
      "\n",
      "2018-05-29 11:08:32,124:INFO: - Eval metrics : PSNR: 16.749 ; SSIM: 0.589\n",
      "\n",
      "2018-05-29 11:08:32,125:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:32,125:INFO: Epoch 15/100\n",
      "\n",
      "2018-05-29 11:08:33,104:INFO: - Train metrics: PSNR: 19.875 ; SSIM: 0.824 ; g_loss: 0.014 ; d_loss: 0.514\n",
      "\n",
      "2018-05-29 11:08:33,827:INFO: - Eval metrics : PSNR: 16.503 ; SSIM: 0.597\n",
      "\n",
      "2018-05-29 11:08:33,829:INFO: Epoch 16/100\n",
      "\n",
      "2018-05-29 11:08:34,802:INFO: - Train metrics: PSNR: 20.045 ; SSIM: 0.822 ; g_loss: 0.014 ; d_loss: 0.500\n",
      "\n",
      "2018-05-29 11:08:35,566:INFO: - Eval metrics : PSNR: 16.499 ; SSIM: 0.612\n",
      "\n",
      "2018-05-29 11:08:35,568:INFO: Epoch 17/100\n",
      "\n",
      "2018-05-29 11:08:36,562:INFO: - Train metrics: PSNR: 20.196 ; SSIM: 0.831 ; g_loss: 0.013 ; d_loss: 0.850\n",
      "\n",
      "2018-05-29 11:08:37,330:INFO: - Eval metrics : PSNR: 17.256 ; SSIM: 0.633\n",
      "\n",
      "2018-05-29 11:08:37,331:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:08:37,332:INFO: Epoch 18/100\n",
      "\n",
      "2018-05-29 11:08:38,327:INFO: - Train metrics: PSNR: 20.563 ; SSIM: 0.842 ; g_loss: 0.013 ; d_loss: 0.508\n",
      "\n",
      "2018-05-29 11:17:12,374:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:17:12,374:INFO: - done.\n",
      "\n",
      "2018-05-29 11:17:19,798:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:17:19,798:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:17:20,749:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 11:17:21,527:INFO: - Eval metrics : PSNR: 11.341 ; SSIM: 0.508\n",
      "\n",
      "2018-05-29 11:17:21,789:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:17:21,790:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 11:17:22,674:INFO: - Train metrics: PSNR: 13.469 ; SSIM: 0.588 ; g_loss: 0.050 ; d_loss: 0.960\n",
      "\n",
      "2018-05-29 11:17:23,438:INFO: - Eval metrics : PSNR: 11.413 ; SSIM: 0.495\n",
      "\n",
      "2018-05-29 11:17:24,344:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:17:24,344:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 11:17:25,221:INFO: - Train metrics: PSNR: 15.188 ; SSIM: 0.575 ; g_loss: 0.035 ; d_loss: 0.895\n",
      "\n",
      "2018-05-29 11:17:25,992:INFO: - Eval metrics : PSNR: 11.556 ; SSIM: 0.491\n",
      "\n",
      "2018-05-29 11:17:27,000:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:17:27,001:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-29 11:17:27,876:INFO: - Train metrics: PSNR: 15.864 ; SSIM: 0.552 ; g_loss: 0.032 ; d_loss: 0.904\n",
      "\n",
      "2018-05-29 11:17:28,645:INFO: - Eval metrics : PSNR: 11.986 ; SSIM: 0.509\n",
      "\n",
      "2018-05-29 11:17:29,540:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:17:29,540:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-29 11:17:40,864:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:17:40,865:INFO: - done.\n",
      "\n",
      "2018-05-29 11:17:48,247:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:17:48,247:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:17:49,205:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979\n",
      "\n",
      "2018-05-29 11:17:49,966:INFO: - Eval metrics : PSNR: 11.341 ; SSIM: 0.508\n",
      "\n",
      "2018-05-29 11:17:50,990:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:17:50,991:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 11:17:51,859:INFO: - Train metrics: PSNR: 13.469 ; SSIM: 0.588 ; g_loss: 0.050 ; d_loss: 0.960\n",
      "\n",
      "2018-05-29 11:17:52,634:INFO: - Eval metrics : PSNR: 11.414 ; SSIM: 0.495\n",
      "\n",
      "2018-05-29 11:17:53,518:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:17:53,518:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 11:17:54,395:INFO: - Train metrics: PSNR: 15.187 ; SSIM: 0.575 ; g_loss: 0.035 ; d_loss: 0.895\n",
      "\n",
      "2018-05-29 11:17:55,179:INFO: - Eval metrics : PSNR: 11.556 ; SSIM: 0.491\n",
      "\n",
      "2018-05-29 11:17:56,058:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:17:56,276:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-29 11:17:57,146:INFO: - Train metrics: PSNR: 15.863 ; SSIM: 0.552 ; g_loss: 0.032 ; d_loss: 0.901\n",
      "\n",
      "2018-05-29 11:17:57,930:INFO: - Eval metrics : PSNR: 11.987 ; SSIM: 0.509\n",
      "\n",
      "2018-05-29 11:17:58,882:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:17:58,883:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-29 11:17:59,760:INFO: - Train metrics: PSNR: 17.319 ; SSIM: 0.638 ; g_loss: 0.023 ; d_loss: 0.835\n",
      "\n",
      "2018-05-29 11:18:00,534:INFO: - Eval metrics : PSNR: 12.148 ; SSIM: 0.524\n",
      "\n",
      "2018-05-29 11:18:01,409:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:01,410:INFO: Epoch 6/100\n",
      "\n",
      "2018-05-29 11:18:02,291:INFO: - Train metrics: PSNR: 17.139 ; SSIM: 0.657 ; g_loss: 0.024 ; d_loss: 0.987\n",
      "\n",
      "2018-05-29 11:18:03,071:INFO: - Eval metrics : PSNR: 12.162 ; SSIM: 0.520\n",
      "\n",
      "2018-05-29 11:18:04,021:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:04,021:INFO: Epoch 7/100\n",
      "\n",
      "2018-05-29 11:18:04,817:INFO: - Train metrics: PSNR: 17.086 ; SSIM: 0.695 ; g_loss: 0.023 ; d_loss: 0.860\n",
      "\n",
      "2018-05-29 11:18:05,577:INFO: - Eval metrics : PSNR: 12.773 ; SSIM: 0.508\n",
      "\n",
      "2018-05-29 11:18:06,458:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:06,459:INFO: Epoch 8/100\n",
      "\n",
      "2018-05-29 11:18:07,260:INFO: - Train metrics: PSNR: 17.609 ; SSIM: 0.700 ; g_loss: 0.022 ; d_loss: 0.868\n",
      "\n",
      "2018-05-29 11:18:07,951:INFO: - Eval metrics : PSNR: 13.694 ; SSIM: 0.537\n",
      "\n",
      "2018-05-29 11:18:08,901:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:08,902:INFO: Epoch 9/100\n",
      "\n",
      "2018-05-29 11:18:09,701:INFO: - Train metrics: PSNR: 18.241 ; SSIM: 0.720 ; g_loss: 0.019 ; d_loss: 0.902\n",
      "\n",
      "2018-05-29 11:18:10,380:INFO: - Eval metrics : PSNR: 14.078 ; SSIM: 0.532\n",
      "\n",
      "2018-05-29 11:18:11,323:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:11,324:INFO: Epoch 10/100\n",
      "\n",
      "2018-05-29 11:18:12,120:INFO: - Train metrics: PSNR: 18.270 ; SSIM: 0.735 ; g_loss: 0.019 ; d_loss: 0.850\n",
      "\n",
      "2018-05-29 11:18:12,870:INFO: - Eval metrics : PSNR: 13.896 ; SSIM: 0.508\n",
      "\n",
      "2018-05-29 11:18:13,222:INFO: Epoch 11/100\n",
      "\n",
      "2018-05-29 11:18:14,029:INFO: - Train metrics: PSNR: 19.053 ; SSIM: 0.767 ; g_loss: 0.016 ; d_loss: 0.754\n",
      "\n",
      "2018-05-29 11:18:14,715:INFO: - Eval metrics : PSNR: 14.702 ; SSIM: 0.520\n",
      "\n",
      "2018-05-29 11:18:15,607:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:15,608:INFO: Epoch 12/100\n",
      "\n",
      "2018-05-29 11:18:16,409:INFO: - Train metrics: PSNR: 19.076 ; SSIM: 0.752 ; g_loss: 0.016 ; d_loss: 0.671\n",
      "\n",
      "2018-05-29 11:18:17,190:INFO: - Eval metrics : PSNR: 15.856 ; SSIM: 0.558\n",
      "\n",
      "2018-05-29 11:18:18,139:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:18,140:INFO: Epoch 13/100\n",
      "\n",
      "2018-05-29 11:18:18,942:INFO: - Train metrics: PSNR: 19.644 ; SSIM: 0.770 ; g_loss: 0.015 ; d_loss: 0.609\n",
      "\n",
      "2018-05-29 11:18:19,736:INFO: - Eval metrics : PSNR: 16.615 ; SSIM: 0.585\n",
      "\n",
      "2018-05-29 11:18:20,765:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:20,766:INFO: Epoch 14/100\n",
      "\n",
      "2018-05-29 11:18:21,647:INFO: - Train metrics: PSNR: 19.752 ; SSIM: 0.809 ; g_loss: 0.015 ; d_loss: 0.528\n",
      "\n",
      "2018-05-29 11:18:22,422:INFO: - Eval metrics : PSNR: 16.777 ; SSIM: 0.590\n",
      "\n",
      "2018-05-29 11:18:23,356:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:23,357:INFO: Epoch 15/100\n",
      "\n",
      "2018-05-29 11:18:24,237:INFO: - Train metrics: PSNR: 19.860 ; SSIM: 0.825 ; g_loss: 0.014 ; d_loss: 0.945\n",
      "\n",
      "2018-05-29 11:18:25,012:INFO: - Eval metrics : PSNR: 16.523 ; SSIM: 0.598\n",
      "\n",
      "2018-05-29 11:18:25,343:INFO: Epoch 16/100\n",
      "\n",
      "2018-05-29 11:18:26,221:INFO: - Train metrics: PSNR: 20.034 ; SSIM: 0.821 ; g_loss: 0.014 ; d_loss: 0.722\n",
      "\n",
      "2018-05-29 11:18:27,002:INFO: - Eval metrics : PSNR: 16.479 ; SSIM: 0.612\n",
      "\n",
      "2018-05-29 11:18:27,476:INFO: Epoch 17/100\n",
      "\n",
      "2018-05-29 11:18:28,353:INFO: - Train metrics: PSNR: 20.149 ; SSIM: 0.829 ; g_loss: 0.014 ; d_loss: 0.425\n",
      "\n",
      "2018-05-29 11:18:29,139:INFO: - Eval metrics : PSNR: 17.217 ; SSIM: 0.633\n",
      "\n",
      "2018-05-29 11:18:30,069:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:30,070:INFO: Epoch 18/100\n",
      "\n",
      "2018-05-29 11:18:30,944:INFO: - Train metrics: PSNR: 20.533 ; SSIM: 0.842 ; g_loss: 0.013 ; d_loss: 0.806\n",
      "\n",
      "2018-05-29 11:18:31,718:INFO: - Eval metrics : PSNR: 18.154 ; SSIM: 0.655\n",
      "\n",
      "2018-05-29 11:18:32,741:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:32,742:INFO: Epoch 19/100\n",
      "\n",
      "2018-05-29 11:18:33,613:INFO: - Train metrics: PSNR: 20.470 ; SSIM: 0.843 ; g_loss: 0.013 ; d_loss: 0.860\n",
      "\n",
      "2018-05-29 11:18:34,397:INFO: - Eval metrics : PSNR: 18.610 ; SSIM: 0.667\n",
      "\n",
      "2018-05-29 11:18:35,347:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:35,347:INFO: Epoch 20/100\n",
      "\n",
      "2018-05-29 11:18:36,223:INFO: - Train metrics: PSNR: 20.698 ; SSIM: 0.844 ; g_loss: 0.012 ; d_loss: 0.581\n",
      "\n",
      "2018-05-29 11:18:37,001:INFO: - Eval metrics : PSNR: 18.902 ; SSIM: 0.682\n",
      "\n",
      "2018-05-29 11:18:38,026:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:38,027:INFO: Epoch 21/100\n",
      "\n",
      "2018-05-29 11:18:38,900:INFO: - Train metrics: PSNR: 20.819 ; SSIM: 0.851 ; g_loss: 0.012 ; d_loss: 0.359\n",
      "\n",
      "2018-05-29 11:18:39,676:INFO: - Eval metrics : PSNR: 18.908 ; SSIM: 0.705\n",
      "\n",
      "2018-05-29 11:18:40,560:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:40,560:INFO: Epoch 22/100\n",
      "\n",
      "2018-05-29 11:18:41,447:INFO: - Train metrics: PSNR: 21.098 ; SSIM: 0.856 ; g_loss: 0.012 ; d_loss: 0.507\n",
      "\n",
      "2018-05-29 11:18:42,221:INFO: - Eval metrics : PSNR: 18.970 ; SSIM: 0.713\n",
      "\n",
      "2018-05-29 11:18:43,169:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:43,169:INFO: Epoch 23/100\n",
      "\n",
      "2018-05-29 11:18:44,046:INFO: - Train metrics: PSNR: 21.182 ; SSIM: 0.856 ; g_loss: 0.011 ; d_loss: 0.291\n",
      "\n",
      "2018-05-29 11:18:44,811:INFO: - Eval metrics : PSNR: 19.149 ; SSIM: 0.715\n",
      "\n",
      "2018-05-29 11:18:45,749:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:45,749:INFO: Epoch 24/100\n",
      "\n",
      "2018-05-29 11:18:46,619:INFO: - Train metrics: PSNR: 21.220 ; SSIM: 0.863 ; g_loss: 0.011 ; d_loss: 0.323\n",
      "\n",
      "2018-05-29 11:18:47,297:INFO: - Eval metrics : PSNR: 19.387 ; SSIM: 0.733\n",
      "\n",
      "2018-05-29 11:18:48,162:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:48,162:INFO: Epoch 25/100\n",
      "\n",
      "2018-05-29 11:18:48,965:INFO: - Train metrics: PSNR: 21.609 ; SSIM: 0.877 ; g_loss: 0.011 ; d_loss: 0.408\n",
      "\n",
      "2018-05-29 11:18:49,654:INFO: - Eval metrics : PSNR: 20.076 ; SSIM: 0.762\n",
      "\n",
      "2018-05-29 11:18:50,602:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:50,603:INFO: Epoch 26/100\n",
      "\n",
      "2018-05-29 11:18:51,403:INFO: - Train metrics: PSNR: 21.387 ; SSIM: 0.873 ; g_loss: 0.011 ; d_loss: 0.354\n",
      "\n",
      "2018-05-29 11:18:52,099:INFO: - Eval metrics : PSNR: 20.338 ; SSIM: 0.775\n",
      "\n",
      "2018-05-29 11:18:53,042:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:53,043:INFO: Epoch 27/100\n",
      "\n",
      "2018-05-29 11:18:53,844:INFO: - Train metrics: PSNR: 21.298 ; SSIM: 0.869 ; g_loss: 0.011 ; d_loss: 1.149\n",
      "\n",
      "2018-05-29 11:18:54,526:INFO: - Eval metrics : PSNR: 20.258 ; SSIM: 0.756\n",
      "\n",
      "2018-05-29 11:18:54,994:INFO: Epoch 28/100\n",
      "\n",
      "2018-05-29 11:18:55,869:INFO: - Train metrics: PSNR: 21.526 ; SSIM: 0.877 ; g_loss: 0.011 ; d_loss: 0.969\n",
      "\n",
      "2018-05-29 11:18:56,551:INFO: - Eval metrics : PSNR: 20.541 ; SSIM: 0.759\n",
      "\n",
      "2018-05-29 11:18:57,439:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:18:57,440:INFO: Epoch 29/100\n",
      "\n",
      "2018-05-29 11:18:58,240:INFO: - Train metrics: PSNR: 21.570 ; SSIM: 0.877 ; g_loss: 0.011 ; d_loss: 0.670\n",
      "\n",
      "2018-05-29 11:18:58,939:INFO: - Eval metrics : PSNR: 20.133 ; SSIM: 0.755\n",
      "\n",
      "2018-05-29 11:18:59,395:INFO: Epoch 30/100\n",
      "\n",
      "2018-05-29 11:19:00,191:INFO: - Train metrics: PSNR: 21.701 ; SSIM: 0.872 ; g_loss: 0.010 ; d_loss: 0.850\n",
      "\n",
      "2018-05-29 11:19:00,959:INFO: - Eval metrics : PSNR: 19.426 ; SSIM: 0.733\n",
      "\n",
      "2018-05-29 11:19:01,431:INFO: Epoch 31/100\n",
      "\n",
      "2018-05-29 11:19:02,226:INFO: - Train metrics: PSNR: 21.701 ; SSIM: 0.874 ; g_loss: 0.010 ; d_loss: 0.902\n",
      "\n",
      "2018-05-29 11:19:02,907:INFO: - Eval metrics : PSNR: 19.302 ; SSIM: 0.721\n",
      "\n",
      "2018-05-29 11:19:03,362:INFO: Epoch 32/100\n",
      "\n",
      "2018-05-29 11:19:04,159:INFO: - Train metrics: PSNR: 21.985 ; SSIM: 0.883 ; g_loss: 0.010 ; d_loss: 0.576\n",
      "\n",
      "2018-05-29 11:19:04,838:INFO: - Eval metrics : PSNR: 20.004 ; SSIM: 0.739\n",
      "\n",
      "2018-05-29 11:19:05,325:INFO: Epoch 33/100\n",
      "\n",
      "2018-05-29 11:19:06,125:INFO: - Train metrics: PSNR: 22.130 ; SSIM: 0.887 ; g_loss: 0.010 ; d_loss: 0.407\n",
      "\n",
      "2018-05-29 11:19:06,797:INFO: - Eval metrics : PSNR: 20.606 ; SSIM: 0.773\n",
      "\n",
      "2018-05-29 11:19:07,728:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:19:07,728:INFO: Epoch 34/100\n",
      "\n",
      "2018-05-29 11:19:08,527:INFO: - Train metrics: PSNR: 22.100 ; SSIM: 0.885 ; g_loss: 0.010 ; d_loss: 0.936\n",
      "\n",
      "2018-05-29 11:19:09,214:INFO: - Eval metrics : PSNR: 20.793 ; SSIM: 0.784\n",
      "\n",
      "2018-05-29 11:19:10,064:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:19:10,262:INFO: Epoch 35/100\n",
      "\n",
      "2018-05-29 11:19:11,053:INFO: - Train metrics: PSNR: 22.288 ; SSIM: 0.888 ; g_loss: 0.010 ; d_loss: 0.492\n",
      "\n",
      "2018-05-29 11:19:11,742:INFO: - Eval metrics : PSNR: 20.516 ; SSIM: 0.767\n",
      "\n",
      "2018-05-29 11:19:12,094:INFO: Epoch 36/100\n",
      "\n",
      "2018-05-29 11:19:12,897:INFO: - Train metrics: PSNR: 22.349 ; SSIM: 0.887 ; g_loss: 0.010 ; d_loss: 0.329\n",
      "\n",
      "2018-05-29 11:19:13,586:INFO: - Eval metrics : PSNR: 19.912 ; SSIM: 0.765\n",
      "\n",
      "2018-05-29 11:19:14,018:INFO: Epoch 37/100\n",
      "\n",
      "2018-05-29 11:19:14,815:INFO: - Train metrics: PSNR: 22.311 ; SSIM: 0.882 ; g_loss: 0.010 ; d_loss: 0.913\n",
      "\n",
      "2018-05-29 11:19:15,504:INFO: - Eval metrics : PSNR: 19.816 ; SSIM: 0.762\n",
      "\n",
      "2018-05-29 11:19:16,003:INFO: Epoch 38/100\n",
      "\n",
      "2018-05-29 11:19:16,799:INFO: - Train metrics: PSNR: 22.463 ; SSIM: 0.886 ; g_loss: 0.009 ; d_loss: 0.611\n",
      "\n",
      "2018-05-29 11:19:17,489:INFO: - Eval metrics : PSNR: 20.278 ; SSIM: 0.773\n",
      "\n",
      "2018-05-29 11:19:17,979:INFO: Epoch 39/100\n",
      "\n",
      "2018-05-29 11:19:18,853:INFO: - Train metrics: PSNR: 22.435 ; SSIM: 0.885 ; g_loss: 0.009 ; d_loss: 0.337\n",
      "\n",
      "2018-05-29 11:19:19,625:INFO: - Eval metrics : PSNR: 20.631 ; SSIM: 0.773\n",
      "\n",
      "2018-05-29 11:19:19,968:INFO: Epoch 40/100\n",
      "\n",
      "2018-05-29 11:19:20,765:INFO: - Train metrics: PSNR: 22.569 ; SSIM: 0.889 ; g_loss: 0.009 ; d_loss: 0.872\n",
      "\n",
      "2018-05-29 11:19:21,456:INFO: - Eval metrics : PSNR: 20.747 ; SSIM: 0.770\n",
      "\n",
      "2018-05-29 11:19:21,846:INFO: Epoch 41/100\n",
      "\n",
      "2018-05-29 11:19:22,643:INFO: - Train metrics: PSNR: 22.696 ; SSIM: 0.890 ; g_loss: 0.008 ; d_loss: 1.144\n",
      "\n",
      "2018-05-29 11:19:23,412:INFO: - Eval metrics : PSNR: 20.827 ; SSIM: 0.768\n",
      "\n",
      "2018-05-29 11:19:24,438:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:19:24,439:INFO: Epoch 42/100\n",
      "\n",
      "2018-05-29 11:19:25,237:INFO: - Train metrics: PSNR: 22.681 ; SSIM: 0.888 ; g_loss: 0.008 ; d_loss: 0.995\n",
      "\n",
      "2018-05-29 11:19:26,009:INFO: - Eval metrics : PSNR: 20.884 ; SSIM: 0.771\n",
      "\n",
      "2018-05-29 11:19:26,959:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:19:26,959:INFO: Epoch 43/100\n",
      "\n",
      "2018-05-29 11:19:27,762:INFO: - Train metrics: PSNR: 22.786 ; SSIM: 0.892 ; g_loss: 0.008 ; d_loss: 1.025\n",
      "\n",
      "2018-05-29 11:19:28,449:INFO: - Eval metrics : PSNR: 21.083 ; SSIM: 0.782\n",
      "\n",
      "2018-05-29 11:19:29,389:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:19:29,389:INFO: Epoch 44/100\n",
      "\n",
      "2018-05-29 11:19:30,189:INFO: - Train metrics: PSNR: 22.824 ; SSIM: 0.895 ; g_loss: 0.008 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:19:30,885:INFO: - Eval metrics : PSNR: 21.299 ; SSIM: 0.794\n",
      "\n",
      "2018-05-29 11:19:31,900:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:19:31,901:INFO: Epoch 45/100\n",
      "\n",
      "2018-05-29 11:19:32,699:INFO: - Train metrics: PSNR: 22.940 ; SSIM: 0.896 ; g_loss: 0.008 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:19:33,387:INFO: - Eval metrics : PSNR: 21.418 ; SSIM: 0.795\n",
      "\n",
      "2018-05-29 11:19:34,335:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:19:34,336:INFO: Epoch 46/100\n",
      "\n",
      "2018-05-29 11:19:35,212:INFO: - Train metrics: PSNR: 22.993 ; SSIM: 0.897 ; g_loss: 0.008 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:19:35,985:INFO: - Eval metrics : PSNR: 21.306 ; SSIM: 0.781\n",
      "\n",
      "2018-05-29 11:19:36,302:INFO: Epoch 47/100\n",
      "\n",
      "2018-05-29 11:19:37,183:INFO: - Train metrics: PSNR: 23.083 ; SSIM: 0.901 ; g_loss: 0.008 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:19:37,958:INFO: - Eval metrics : PSNR: 21.287 ; SSIM: 0.776\n",
      "\n",
      "2018-05-29 11:19:38,387:INFO: Epoch 48/100\n",
      "\n",
      "2018-05-29 11:19:39,265:INFO: - Train metrics: PSNR: 23.204 ; SSIM: 0.904 ; g_loss: 0.008 ; d_loss: 1.001\n",
      "\n",
      "2018-05-29 11:19:40,050:INFO: - Eval metrics : PSNR: 21.389 ; SSIM: 0.781\n",
      "\n",
      "2018-05-29 11:19:40,418:INFO: Epoch 49/100\n",
      "\n",
      "2018-05-29 11:19:41,298:INFO: - Train metrics: PSNR: 23.218 ; SSIM: 0.905 ; g_loss: 0.008 ; d_loss: 1.001\n",
      "\n",
      "2018-05-29 11:19:42,078:INFO: - Eval metrics : PSNR: 21.417 ; SSIM: 0.781\n",
      "\n",
      "2018-05-29 11:19:42,383:INFO: Epoch 50/100\n",
      "\n",
      "2018-05-29 11:19:43,268:INFO: - Train metrics: PSNR: 23.306 ; SSIM: 0.906 ; g_loss: 0.008 ; d_loss: 1.001\n",
      "\n",
      "2018-05-29 11:19:44,042:INFO: - Eval metrics : PSNR: 21.393 ; SSIM: 0.768\n",
      "\n",
      "2018-05-29 11:19:44,532:INFO: Epoch 51/100\n",
      "\n",
      "2018-05-29 11:19:45,417:INFO: - Train metrics: PSNR: 23.329 ; SSIM: 0.905 ; g_loss: 0.007 ; d_loss: 1.001\n",
      "\n",
      "2018-05-29 11:19:46,194:INFO: - Eval metrics : PSNR: 21.356 ; SSIM: 0.756\n",
      "\n",
      "2018-05-29 11:19:46,534:INFO: Epoch 52/100\n",
      "\n",
      "2018-05-29 11:19:47,413:INFO: - Train metrics: PSNR: 23.404 ; SSIM: 0.906 ; g_loss: 0.007 ; d_loss: 1.001\n",
      "\n",
      "2018-05-29 11:19:48,186:INFO: - Eval metrics : PSNR: 21.436 ; SSIM: 0.763\n",
      "\n",
      "2018-05-29 11:19:49,088:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:19:49,089:INFO: Epoch 53/100\n",
      "\n",
      "2018-05-29 11:19:49,966:INFO: - Train metrics: PSNR: 23.429 ; SSIM: 0.906 ; g_loss: 0.007 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:19:50,738:INFO: - Eval metrics : PSNR: 21.459 ; SSIM: 0.779\n",
      "\n",
      "2018-05-29 11:19:51,695:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:19:51,695:INFO: Epoch 54/100\n",
      "\n",
      "2018-05-29 11:19:52,570:INFO: - Train metrics: PSNR: 23.517 ; SSIM: 0.908 ; g_loss: 0.007 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:19:53,269:INFO: - Eval metrics : PSNR: 21.574 ; SSIM: 0.789\n",
      "\n",
      "2018-05-29 11:19:54,176:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:19:54,177:INFO: Epoch 55/100\n",
      "\n",
      "2018-05-29 11:19:55,053:INFO: - Train metrics: PSNR: 23.541 ; SSIM: 0.907 ; g_loss: 0.007 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:19:55,840:INFO: - Eval metrics : PSNR: 21.727 ; SSIM: 0.792\n",
      "\n",
      "2018-05-29 11:19:56,862:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:19:56,862:INFO: Epoch 56/100\n",
      "\n",
      "2018-05-29 11:19:57,759:INFO: - Train metrics: PSNR: 23.605 ; SSIM: 0.909 ; g_loss: 0.007 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:19:58,536:INFO: - Eval metrics : PSNR: 21.790 ; SSIM: 0.797\n",
      "\n",
      "2018-05-29 11:19:59,428:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:19:59,429:INFO: Epoch 57/100\n",
      "\n",
      "2018-05-29 11:20:00,308:INFO: - Train metrics: PSNR: 23.610 ; SSIM: 0.910 ; g_loss: 0.007 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:01,084:INFO: - Eval metrics : PSNR: 21.774 ; SSIM: 0.800\n",
      "\n",
      "2018-05-29 11:20:01,507:INFO: Epoch 58/100\n",
      "\n",
      "2018-05-29 11:20:02,387:INFO: - Train metrics: PSNR: 23.680 ; SSIM: 0.910 ; g_loss: 0.007 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:03,157:INFO: - Eval metrics : PSNR: 21.869 ; SSIM: 0.799\n",
      "\n",
      "2018-05-29 11:20:04,200:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:04,201:INFO: Epoch 59/100\n",
      "\n",
      "2018-05-29 11:20:05,075:INFO: - Train metrics: PSNR: 23.726 ; SSIM: 0.911 ; g_loss: 0.007 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:05,851:INFO: - Eval metrics : PSNR: 21.909 ; SSIM: 0.795\n",
      "\n",
      "2018-05-29 11:20:06,707:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:06,708:INFO: Epoch 60/100\n",
      "\n",
      "2018-05-29 11:20:07,585:INFO: - Train metrics: PSNR: 23.791 ; SSIM: 0.912 ; g_loss: 0.007 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:08,353:INFO: - Eval metrics : PSNR: 21.896 ; SSIM: 0.800\n",
      "\n",
      "2018-05-29 11:20:08,808:INFO: Epoch 61/100\n",
      "\n",
      "2018-05-29 11:20:09,685:INFO: - Train metrics: PSNR: 23.810 ; SSIM: 0.913 ; g_loss: 0.007 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:10,461:INFO: - Eval metrics : PSNR: 21.924 ; SSIM: 0.806\n",
      "\n",
      "2018-05-29 11:20:11,325:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:11,326:INFO: Epoch 62/100\n",
      "\n",
      "2018-05-29 11:20:12,207:INFO: - Train metrics: PSNR: 23.873 ; SSIM: 0.914 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:12,978:INFO: - Eval metrics : PSNR: 22.020 ; SSIM: 0.804\n",
      "\n",
      "2018-05-29 11:20:13,856:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:13,856:INFO: Epoch 63/100\n",
      "\n",
      "2018-05-29 11:20:14,735:INFO: - Train metrics: PSNR: 23.909 ; SSIM: 0.914 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:15,497:INFO: - Eval metrics : PSNR: 22.024 ; SSIM: 0.792\n",
      "\n",
      "2018-05-29 11:20:16,375:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:16,376:INFO: Epoch 64/100\n",
      "\n",
      "2018-05-29 11:20:17,259:INFO: - Train metrics: PSNR: 23.953 ; SSIM: 0.915 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:17,932:INFO: - Eval metrics : PSNR: 22.019 ; SSIM: 0.788\n",
      "\n",
      "2018-05-29 11:20:18,390:INFO: Epoch 65/100\n",
      "\n",
      "2018-05-29 11:20:19,269:INFO: - Train metrics: PSNR: 24.003 ; SSIM: 0.916 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:20,033:INFO: - Eval metrics : PSNR: 22.112 ; SSIM: 0.797\n",
      "\n",
      "2018-05-29 11:20:21,030:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:21,030:INFO: Epoch 66/100\n",
      "\n",
      "2018-05-29 11:20:21,903:INFO: - Train metrics: PSNR: 24.042 ; SSIM: 0.916 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:22,684:INFO: - Eval metrics : PSNR: 22.193 ; SSIM: 0.805\n",
      "\n",
      "2018-05-29 11:20:23,631:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:23,632:INFO: Epoch 67/100\n",
      "\n",
      "2018-05-29 11:20:24,509:INFO: - Train metrics: PSNR: 24.082 ; SSIM: 0.916 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:25,287:INFO: - Eval metrics : PSNR: 22.250 ; SSIM: 0.807\n",
      "\n",
      "2018-05-29 11:20:26,253:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:26,254:INFO: Epoch 68/100\n",
      "\n",
      "2018-05-29 11:20:27,126:INFO: - Train metrics: PSNR: 24.108 ; SSIM: 0.917 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:27,907:INFO: - Eval metrics : PSNR: 22.238 ; SSIM: 0.805\n",
      "\n",
      "2018-05-29 11:20:28,231:INFO: Epoch 69/100\n",
      "\n",
      "2018-05-29 11:20:29,112:INFO: - Train metrics: PSNR: 24.166 ; SSIM: 0.917 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:29,812:INFO: - Eval metrics : PSNR: 22.288 ; SSIM: 0.803\n",
      "\n",
      "2018-05-29 11:20:30,846:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:30,847:INFO: Epoch 70/100\n",
      "\n",
      "2018-05-29 11:20:31,720:INFO: - Train metrics: PSNR: 24.209 ; SSIM: 0.917 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:32,492:INFO: - Eval metrics : PSNR: 22.282 ; SSIM: 0.797\n",
      "\n",
      "2018-05-29 11:20:32,822:INFO: Epoch 71/100\n",
      "\n",
      "2018-05-29 11:20:33,703:INFO: - Train metrics: PSNR: 24.243 ; SSIM: 0.918 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:34,401:INFO: - Eval metrics : PSNR: 22.237 ; SSIM: 0.794\n",
      "\n",
      "2018-05-29 11:20:34,845:INFO: Epoch 72/100\n",
      "\n",
      "2018-05-29 11:20:35,721:INFO: - Train metrics: PSNR: 24.300 ; SSIM: 0.918 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:36,494:INFO: - Eval metrics : PSNR: 22.432 ; SSIM: 0.811\n",
      "\n",
      "2018-05-29 11:20:37,367:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:37,368:INFO: Epoch 73/100\n",
      "\n",
      "2018-05-29 11:20:38,270:INFO: - Train metrics: PSNR: 24.333 ; SSIM: 0.919 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:39,046:INFO: - Eval metrics : PSNR: 22.487 ; SSIM: 0.817\n",
      "\n",
      "2018-05-29 11:20:39,928:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:39,928:INFO: Epoch 74/100\n",
      "\n",
      "2018-05-29 11:20:40,728:INFO: - Train metrics: PSNR: 24.372 ; SSIM: 0.919 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:41,414:INFO: - Eval metrics : PSNR: 22.548 ; SSIM: 0.818\n",
      "\n",
      "2018-05-29 11:20:42,391:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:42,392:INFO: Epoch 75/100\n",
      "\n",
      "2018-05-29 11:20:43,191:INFO: - Train metrics: PSNR: 24.417 ; SSIM: 0.920 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:43,866:INFO: - Eval metrics : PSNR: 22.532 ; SSIM: 0.816\n",
      "\n",
      "2018-05-29 11:20:44,326:INFO: Epoch 76/100\n",
      "\n",
      "2018-05-29 11:20:45,120:INFO: - Train metrics: PSNR: 24.457 ; SSIM: 0.920 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:45,807:INFO: - Eval metrics : PSNR: 22.626 ; SSIM: 0.819\n",
      "\n",
      "2018-05-29 11:20:46,787:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:46,788:INFO: Epoch 77/100\n",
      "\n",
      "2018-05-29 11:20:47,584:INFO: - Train metrics: PSNR: 24.482 ; SSIM: 0.921 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:48,277:INFO: - Eval metrics : PSNR: 22.548 ; SSIM: 0.816\n",
      "\n",
      "2018-05-29 11:20:48,749:INFO: Epoch 78/100\n",
      "\n",
      "2018-05-29 11:20:49,541:INFO: - Train metrics: PSNR: 24.507 ; SSIM: 0.921 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:50,288:INFO: - Eval metrics : PSNR: 22.712 ; SSIM: 0.822\n",
      "\n",
      "2018-05-29 11:20:51,153:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:20:51,154:INFO: Epoch 79/100\n",
      "\n",
      "2018-05-29 11:20:51,952:INFO: - Train metrics: PSNR: 24.448 ; SSIM: 0.921 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:52,635:INFO: - Eval metrics : PSNR: 22.570 ; SSIM: 0.818\n",
      "\n",
      "2018-05-29 11:20:53,081:INFO: Epoch 80/100\n",
      "\n",
      "2018-05-29 11:20:53,879:INFO: - Train metrics: PSNR: 24.259 ; SSIM: 0.919 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:54,570:INFO: - Eval metrics : PSNR: 22.570 ; SSIM: 0.818\n",
      "\n",
      "2018-05-29 11:20:55,055:INFO: Epoch 81/100\n",
      "\n",
      "2018-05-29 11:20:55,854:INFO: - Train metrics: PSNR: 23.664 ; SSIM: 0.909 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:56,547:INFO: - Eval metrics : PSNR: 21.701 ; SSIM: 0.765\n",
      "\n",
      "2018-05-29 11:20:57,035:INFO: Epoch 82/100\n",
      "\n",
      "2018-05-29 11:20:57,831:INFO: - Train metrics: PSNR: 23.146 ; SSIM: 0.905 ; g_loss: 0.007 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:20:58,605:INFO: - Eval metrics : PSNR: 21.461 ; SSIM: 0.771\n",
      "\n",
      "2018-05-29 11:20:59,076:INFO: Epoch 83/100\n",
      "\n",
      "2018-05-29 11:20:59,869:INFO: - Train metrics: PSNR: 23.264 ; SSIM: 0.900 ; g_loss: 0.007 ; d_loss: 0.999\n",
      "\n",
      "2018-05-29 11:21:00,558:INFO: - Eval metrics : PSNR: 21.458 ; SSIM: 0.744\n",
      "\n",
      "2018-05-29 11:21:00,974:INFO: Epoch 84/100\n",
      "\n",
      "2018-05-29 11:21:01,773:INFO: - Train metrics: PSNR: 24.623 ; SSIM: 0.921 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:02,445:INFO: - Eval metrics : PSNR: 22.653 ; SSIM: 0.821\n",
      "\n",
      "2018-05-29 11:21:02,838:INFO: Epoch 85/100\n",
      "\n",
      "2018-05-29 11:21:03,634:INFO: - Train metrics: PSNR: 23.865 ; SSIM: 0.917 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:04,323:INFO: - Eval metrics : PSNR: 22.040 ; SSIM: 0.799\n",
      "\n",
      "2018-05-29 11:21:04,814:INFO: Epoch 86/100\n",
      "\n",
      "2018-05-29 11:21:05,622:INFO: - Train metrics: PSNR: 24.307 ; SSIM: 0.919 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:06,318:INFO: - Eval metrics : PSNR: 22.494 ; SSIM: 0.818\n",
      "\n",
      "2018-05-29 11:21:06,715:INFO: Epoch 87/100\n",
      "\n",
      "2018-05-29 11:21:07,518:INFO: - Train metrics: PSNR: 24.475 ; SSIM: 0.918 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:08,228:INFO: - Eval metrics : PSNR: 22.514 ; SSIM: 0.789\n",
      "\n",
      "2018-05-29 11:21:08,708:INFO: Epoch 88/100\n",
      "\n",
      "2018-05-29 11:21:09,585:INFO: - Train metrics: PSNR: 24.301 ; SSIM: 0.920 ; g_loss: 0.006 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:10,370:INFO: - Eval metrics : PSNR: 22.266 ; SSIM: 0.818\n",
      "\n",
      "2018-05-29 11:21:10,761:INFO: Epoch 89/100\n",
      "\n",
      "2018-05-29 11:21:11,636:INFO: - Train metrics: PSNR: 24.637 ; SSIM: 0.920 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:12,409:INFO: - Eval metrics : PSNR: 22.698 ; SSIM: 0.817\n",
      "\n",
      "2018-05-29 11:21:12,712:INFO: Epoch 90/100\n",
      "\n",
      "2018-05-29 11:21:13,590:INFO: - Train metrics: PSNR: 24.482 ; SSIM: 0.923 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:14,365:INFO: - Eval metrics : PSNR: 22.827 ; SSIM: 0.817\n",
      "\n",
      "2018-05-29 11:21:15,339:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:21:15,340:INFO: Epoch 91/100\n",
      "\n",
      "2018-05-29 11:21:16,218:INFO: - Train metrics: PSNR: 24.675 ; SSIM: 0.917 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:16,986:INFO: - Eval metrics : PSNR: 22.493 ; SSIM: 0.776\n",
      "\n",
      "2018-05-29 11:21:17,464:INFO: Epoch 92/100\n",
      "\n",
      "2018-05-29 11:21:18,345:INFO: - Train metrics: PSNR: 24.679 ; SSIM: 0.923 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:19,111:INFO: - Eval metrics : PSNR: 22.732 ; SSIM: 0.821\n",
      "\n",
      "2018-05-29 11:21:19,519:INFO: Epoch 93/100\n",
      "\n",
      "2018-05-29 11:21:20,394:INFO: - Train metrics: PSNR: 24.724 ; SSIM: 0.921 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:21,158:INFO: - Eval metrics : PSNR: 22.770 ; SSIM: 0.813\n",
      "\n",
      "2018-05-29 11:21:21,496:INFO: Epoch 94/100\n",
      "\n",
      "2018-05-29 11:21:22,376:INFO: - Train metrics: PSNR: 24.840 ; SSIM: 0.924 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:23,068:INFO: - Eval metrics : PSNR: 22.771 ; SSIM: 0.799\n",
      "\n",
      "2018-05-29 11:21:23,484:INFO: Epoch 95/100\n",
      "\n",
      "2018-05-29 11:21:24,358:INFO: - Train metrics: PSNR: 24.751 ; SSIM: 0.922 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:25,133:INFO: - Eval metrics : PSNR: 22.427 ; SSIM: 0.784\n",
      "\n",
      "2018-05-29 11:21:25,626:INFO: Epoch 96/100\n",
      "\n",
      "2018-05-29 11:21:26,501:INFO: - Train metrics: PSNR: 25.014 ; SSIM: 0.927 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:27,275:INFO: - Eval metrics : PSNR: 23.047 ; SSIM: 0.831\n",
      "\n",
      "2018-05-29 11:21:28,146:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:21:28,147:INFO: Epoch 97/100\n",
      "\n",
      "2018-05-29 11:21:28,949:INFO: - Train metrics: PSNR: 24.818 ; SSIM: 0.924 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:29,677:INFO: - Eval metrics : PSNR: 22.697 ; SSIM: 0.814\n",
      "\n",
      "2018-05-29 11:21:29,999:INFO: Epoch 98/100\n",
      "\n",
      "2018-05-29 11:21:30,804:INFO: - Train metrics: PSNR: 25.077 ; SSIM: 0.928 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:31,499:INFO: - Eval metrics : PSNR: 23.047 ; SSIM: 0.834\n",
      "\n",
      "2018-05-29 11:21:31,961:INFO: Epoch 99/100\n",
      "\n",
      "2018-05-29 11:21:32,764:INFO: - Train metrics: PSNR: 24.942 ; SSIM: 0.922 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:33,459:INFO: - Eval metrics : PSNR: 22.429 ; SSIM: 0.771\n",
      "\n",
      "2018-05-29 11:21:33,806:INFO: Epoch 100/100\n",
      "\n",
      "2018-05-29 11:21:34,679:INFO: - Train metrics: PSNR: 25.153 ; SSIM: 0.929 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:21:35,426:INFO: - Eval metrics : PSNR: 23.163 ; SSIM: 0.832\n",
      "\n",
      "2018-05-29 11:21:36,297:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:28:30,194:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:28:30,194:INFO: - done.\n",
      "\n",
      "2018-05-29 11:28:37,860:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:28:57,410:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:28:57,410:INFO: - done.\n",
      "\n",
      "2018-05-29 11:29:04,774:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:29:04,774:INFO: Restoring parameters from experiments/gan_model/best_g.pth.tar\n",
      "\n",
      "2018-05-29 11:29:04,974:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:29:05,889:INFO: - Train metrics: PSNR: 25.079 ; SSIM: 0.927 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:29:06,675:INFO: - Eval metrics : PSNR: 22.740 ; SSIM: 0.804\n",
      "\n",
      "2018-05-29 11:29:07,724:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:29:07,725:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 11:29:08,525:INFO: - Train metrics: PSNR: 25.194 ; SSIM: 0.929 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:29:09,229:INFO: - Eval metrics : PSNR: 23.172 ; SSIM: 0.832\n",
      "\n",
      "2018-05-29 11:29:10,174:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:29:10,174:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 11:29:11,004:INFO: - Train metrics: PSNR: 25.194 ; SSIM: 0.927 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:29:11,783:INFO: - Eval metrics : PSNR: 23.045 ; SSIM: 0.814\n",
      "\n",
      "2018-05-29 11:29:12,112:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-29 11:29:12,918:INFO: - Train metrics: PSNR: 25.224 ; SSIM: 0.930 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:29:13,609:INFO: - Eval metrics : PSNR: 23.218 ; SSIM: 0.837\n",
      "\n",
      "2018-05-29 11:29:14,539:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:29:14,539:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-29 11:29:15,340:INFO: - Train metrics: PSNR: 25.282 ; SSIM: 0.930 ; g_loss: 0.005 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:29:16,095:INFO: - Eval metrics : PSNR: 23.267 ; SSIM: 0.833\n",
      "\n",
      "2018-05-29 11:29:17,115:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:29:17,115:INFO: Epoch 6/100\n",
      "\n",
      "2018-05-29 11:29:17,919:INFO: - Train metrics: PSNR: 25.292 ; SSIM: 0.930 ; g_loss: 0.004 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:29:18,625:INFO: - Eval metrics : PSNR: 23.270 ; SSIM: 0.834\n",
      "\n",
      "2018-05-29 11:29:19,615:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:29:19,616:INFO: Epoch 7/100\n",
      "\n",
      "2018-05-29 11:29:20,494:INFO: - Train metrics: PSNR: 25.363 ; SSIM: 0.931 ; g_loss: 0.004 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:29:21,263:INFO: - Eval metrics : PSNR: 23.166 ; SSIM: 0.830\n",
      "\n",
      "2018-05-29 11:29:21,689:INFO: Epoch 8/100\n",
      "\n",
      "2018-05-29 11:29:22,495:INFO: - Train metrics: PSNR: 25.341 ; SSIM: 0.929 ; g_loss: 0.004 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:29:23,200:INFO: - Eval metrics : PSNR: 23.114 ; SSIM: 0.812\n",
      "\n",
      "2018-05-29 11:29:23,701:INFO: Epoch 9/100\n",
      "\n",
      "2018-05-29 11:29:24,503:INFO: - Train metrics: PSNR: 25.397 ; SSIM: 0.931 ; g_loss: 0.004 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:29:25,213:INFO: - Eval metrics : PSNR: 23.333 ; SSIM: 0.837\n",
      "\n",
      "2018-05-29 11:29:26,259:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:29:26,259:INFO: Epoch 10/100\n",
      "\n",
      "2018-05-29 11:29:27,060:INFO: - Train metrics: PSNR: 25.415 ; SSIM: 0.931 ; g_loss: 0.004 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:29:27,753:INFO: - Eval metrics : PSNR: 23.370 ; SSIM: 0.833\n",
      "\n",
      "2018-05-29 11:29:28,699:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:29:28,699:INFO: Epoch 11/100\n",
      "\n",
      "2018-05-29 11:29:29,501:INFO: - Train metrics: PSNR: 25.475 ; SSIM: 0.932 ; g_loss: 0.004 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:29:30,202:INFO: - Eval metrics : PSNR: 23.181 ; SSIM: 0.830\n",
      "\n",
      "2018-05-29 11:29:30,670:INFO: Epoch 12/100\n",
      "\n",
      "2018-05-29 11:29:31,470:INFO: - Train metrics: PSNR: 25.488 ; SSIM: 0.931 ; g_loss: 0.004 ; d_loss: 1.000\n",
      "\n",
      "2018-05-29 11:29:32,161:INFO: - Eval metrics : PSNR: 23.056 ; SSIM: 0.809\n",
      "\n",
      "2018-05-29 11:29:32,566:INFO: Epoch 13/100\n",
      "\n",
      "2018-05-29 11:39:35,218:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:39:36,188:INFO: - done.\n",
      "\n",
      "2018-05-29 11:39:43,632:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:39:43,633:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:40:47,278:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:40:47,482:INFO: - done.\n",
      "\n",
      "2018-05-29 11:40:54,853:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:40:54,853:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:43:39,145:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:43:39,348:INFO: - done.\n",
      "\n",
      "2018-05-29 11:43:46,689:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:43:46,689:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:53:10,887:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:53:10,887:INFO: - done.\n",
      "\n",
      "2018-05-29 11:53:18,198:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:53:18,198:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 11:53:19,159:INFO: - Train metrics: PSNR: 11.353 ; SSIM: 0.582 ; g_loss: 0.081 ; d_loss: 0.979 ; mse_loss: 5018.734\n",
      "\n",
      "2018-05-29 11:53:19,850:INFO: - Eval metrics : PSNR: 11.341 ; SSIM: 0.508 ; mse_loss: 5096.159\n",
      "\n",
      "2018-05-29 11:53:20,887:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:53:20,888:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 11:53:21,704:INFO: - Train metrics: PSNR: 13.474 ; SSIM: 0.588 ; g_loss: 0.050 ; d_loss: 0.960 ; mse_loss: 3056.065\n",
      "\n",
      "2018-05-29 11:53:22,410:INFO: - Eval metrics : PSNR: 11.413 ; SSIM: 0.495 ; mse_loss: 4975.423\n",
      "\n",
      "2018-05-29 11:53:23,296:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:53:23,297:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 11:53:24,184:INFO: - Train metrics: PSNR: 15.183 ; SSIM: 0.575 ; g_loss: 0.035 ; d_loss: 0.895 ; mse_loss: 2069.466\n",
      "\n",
      "2018-05-29 11:53:24,875:INFO: - Eval metrics : PSNR: 11.555 ; SSIM: 0.491 ; mse_loss: 4771.399\n",
      "\n",
      "2018-05-29 11:53:25,909:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:53:25,909:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-29 11:53:26,796:INFO: - Train metrics: PSNR: 15.865 ; SSIM: 0.552 ; g_loss: 0.032 ; d_loss: 0.901 ; mse_loss: 1835.917\n",
      "\n",
      "2018-05-29 11:53:27,543:INFO: - Eval metrics : PSNR: 11.986 ; SSIM: 0.509 ; mse_loss: 4276.010\n",
      "\n",
      "2018-05-29 11:53:28,589:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:53:28,591:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-29 11:53:29,404:INFO: - Train metrics: PSNR: 17.315 ; SSIM: 0.638 ; g_loss: 0.023 ; d_loss: 0.835 ; mse_loss: 1291.067\n",
      "\n",
      "2018-05-29 11:53:30,092:INFO: - Eval metrics : PSNR: 12.146 ; SSIM: 0.524 ; mse_loss: 4111.394\n",
      "\n",
      "2018-05-29 11:53:31,106:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:53:31,106:INFO: Epoch 6/100\n",
      "\n",
      "2018-05-29 11:53:31,918:INFO: - Train metrics: PSNR: 17.121 ; SSIM: 0.656 ; g_loss: 0.024 ; d_loss: 0.990 ; mse_loss: 1318.014\n",
      "\n",
      "2018-05-29 11:53:32,615:INFO: - Eval metrics : PSNR: 12.156 ; SSIM: 0.520 ; mse_loss: 4060.089\n",
      "\n",
      "2018-05-29 11:53:33,565:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:53:33,566:INFO: Epoch 7/100\n",
      "\n",
      "2018-05-29 11:53:34,376:INFO: - Train metrics: PSNR: 17.098 ; SSIM: 0.695 ; g_loss: 0.023 ; d_loss: 0.892 ; mse_loss: 1291.224\n",
      "\n",
      "2018-05-29 11:53:35,163:INFO: - Eval metrics : PSNR: 12.769 ; SSIM: 0.509 ; mse_loss: 3576.692\n",
      "\n",
      "2018-05-29 11:53:36,163:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:53:36,164:INFO: Epoch 8/100\n",
      "\n",
      "2018-05-29 11:53:36,970:INFO: - Train metrics: PSNR: 17.637 ; SSIM: 0.701 ; g_loss: 0.021 ; d_loss: 0.855 ; mse_loss: 1161.281\n",
      "\n",
      "2018-05-29 11:53:37,665:INFO: - Eval metrics : PSNR: 13.698 ; SSIM: 0.536 ; mse_loss: 2829.714\n",
      "\n",
      "2018-05-29 11:53:38,618:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:53:38,618:INFO: Epoch 9/100\n",
      "\n",
      "2018-05-29 11:53:39,449:INFO: - Train metrics: PSNR: 18.241 ; SSIM: 0.720 ; g_loss: 0.019 ; d_loss: 0.872 ; mse_loss: 997.850\n",
      "\n",
      "2018-05-29 11:53:40,136:INFO: - Eval metrics : PSNR: 14.078 ; SSIM: 0.532 ; mse_loss: 2593.786\n",
      "\n",
      "2018-05-29 11:53:41,009:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:53:41,010:INFO: Epoch 10/100\n",
      "\n",
      "2018-05-29 11:53:41,833:INFO: - Train metrics: PSNR: 18.285 ; SSIM: 0.735 ; g_loss: 0.019 ; d_loss: 0.852 ; mse_loss: 974.577\n",
      "\n",
      "2018-05-29 11:53:42,544:INFO: - Eval metrics : PSNR: 13.896 ; SSIM: 0.507 ; mse_loss: 2734.531\n",
      "\n",
      "2018-05-29 11:53:42,933:INFO: Epoch 11/100\n",
      "\n",
      "2018-05-29 11:53:43,756:INFO: - Train metrics: PSNR: 19.042 ; SSIM: 0.767 ; g_loss: 0.016 ; d_loss: 0.757 ; mse_loss: 832.682\n",
      "\n",
      "2018-05-29 11:53:44,479:INFO: - Eval metrics : PSNR: 14.683 ; SSIM: 0.519 ; mse_loss: 2273.013\n",
      "\n",
      "2018-05-29 11:53:45,422:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:53:45,422:INFO: Epoch 12/100\n",
      "\n",
      "2018-05-29 11:53:46,238:INFO: - Train metrics: PSNR: 19.096 ; SSIM: 0.753 ; g_loss: 0.016 ; d_loss: 0.696 ; mse_loss: 819.688\n",
      "\n",
      "2018-05-29 11:53:46,942:INFO: - Eval metrics : PSNR: 15.854 ; SSIM: 0.557 ; mse_loss: 1729.605\n",
      "\n",
      "2018-05-29 11:53:47,841:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 11:53:47,841:INFO: Epoch 13/100\n",
      "\n",
      "2018-05-29 11:53:48,660:INFO: - Train metrics: PSNR: 19.632 ; SSIM: 0.770 ; g_loss: 0.015 ; d_loss: 0.658 ; mse_loss: 728.879\n",
      "\n",
      "2018-05-29 11:53:55,197:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 11:53:55,397:INFO: - done.\n",
      "\n",
      "2018-05-29 11:54:03,107:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 11:54:03,108:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 12:11:26,521:INFO: - Train metrics: PSNR: 22.062 ; SSIM: 0.806 ; g_loss: 0.024 ; d_loss: 0.988 ; mse_loss: 1405.485\n",
      "\n",
      "2018-05-29 12:11:30,130:INFO: - Eval metrics : PSNR: 25.721 ; SSIM: 0.908 ; mse_loss: 189.788\n",
      "\n",
      "2018-05-29 12:11:31,159:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 12:11:31,159:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 12:28:54,619:INFO: - Train metrics: PSNR: 25.684 ; SSIM: 0.903 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 195.128\n",
      "\n",
      "2018-05-29 12:28:58,213:INFO: - Eval metrics : PSNR: 25.946 ; SSIM: 0.913 ; mse_loss: 180.735\n",
      "\n",
      "2018-05-29 12:28:59,262:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 12:28:59,263:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 12:46:23,721:INFO: - Train metrics: PSNR: 26.433 ; SSIM: 0.915 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 166.199\n",
      "\n",
      "2018-05-29 12:46:27,309:INFO: - Eval metrics : PSNR: 26.156 ; SSIM: 0.912 ; mse_loss: 173.374\n",
      "\n",
      "2018-05-29 12:46:28,335:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 12:46:28,336:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-29 13:03:53,037:INFO: - Train metrics: PSNR: 26.214 ; SSIM: 0.922 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 170.055\n",
      "\n",
      "2018-05-29 13:03:56,620:INFO: - Eval metrics : PSNR: 26.272 ; SSIM: 0.918 ; mse_loss: 169.107\n",
      "\n",
      "2018-05-29 13:03:57,600:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 13:03:57,601:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-29 13:09:21,416:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 13:09:21,615:INFO: - done.\n",
      "\n",
      "2018-05-29 13:09:29,250:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 13:09:29,250:INFO: Restoring parameters from experiments/gan_model/best_g.pth.tar\n",
      "\n",
      "2018-05-29 13:09:29,448:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 13:26:52,668:INFO: - Train metrics: PSNR: 26.367 ; SSIM: 0.926 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 162.097\n",
      "\n",
      "2018-05-29 13:26:56,299:INFO: - Eval metrics : PSNR: 26.170 ; SSIM: 0.917 ; mse_loss: 172.422\n",
      "\n",
      "2018-05-29 13:26:57,288:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 13:26:57,289:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 13:44:20,660:INFO: - Train metrics: PSNR: 26.045 ; SSIM: 0.912 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 180.367\n",
      "\n",
      "2018-05-29 13:44:24,314:INFO: - Eval metrics : PSNR: 26.349 ; SSIM: 0.918 ; mse_loss: 166.362\n",
      "\n",
      "2018-05-29 13:44:25,248:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 13:44:25,249:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 14:01:47,923:INFO: - Train metrics: PSNR: 26.642 ; SSIM: 0.919 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 158.352\n",
      "\n",
      "2018-05-29 14:01:51,559:INFO: - Eval metrics : PSNR: 26.381 ; SSIM: 0.918 ; mse_loss: 165.230\n",
      "\n",
      "2018-05-29 14:01:52,582:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 14:01:52,582:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-29 14:19:17,473:INFO: - Train metrics: PSNR: 26.414 ; SSIM: 0.925 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 163.192\n",
      "\n",
      "2018-05-29 14:19:21,021:INFO: - Eval metrics : PSNR: 26.417 ; SSIM: 0.920 ; mse_loss: 164.159\n",
      "\n",
      "2018-05-29 14:19:22,055:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 14:19:22,055:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-29 14:36:44,824:INFO: - Train metrics: PSNR: 26.765 ; SSIM: 0.926 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 153.290\n",
      "\n",
      "2018-05-29 14:36:48,418:INFO: - Eval metrics : PSNR: 26.442 ; SSIM: 0.920 ; mse_loss: 163.300\n",
      "\n",
      "2018-05-29 14:36:49,422:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 14:36:49,423:INFO: Epoch 6/100\n",
      "\n",
      "2018-05-29 14:54:10,697:INFO: - Train metrics: PSNR: 26.812 ; SSIM: 0.925 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 150.054\n",
      "\n",
      "2018-05-29 14:54:14,321:INFO: - Eval metrics : PSNR: 26.452 ; SSIM: 0.919 ; mse_loss: 162.908\n",
      "\n",
      "2018-05-29 14:54:15,306:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 14:54:15,307:INFO: Epoch 7/100\n",
      "\n",
      "2018-05-29 15:11:39,108:INFO: - Train metrics: PSNR: 26.081 ; SSIM: 0.924 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 183.195\n",
      "\n",
      "2018-05-29 15:11:42,741:INFO: - Eval metrics : PSNR: 26.428 ; SSIM: 0.920 ; mse_loss: 163.615\n",
      "\n",
      "2018-05-29 15:11:43,178:INFO: Epoch 8/100\n",
      "\n",
      "2018-05-29 15:29:05,400:INFO: - Train metrics: PSNR: 26.698 ; SSIM: 0.930 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 152.407\n",
      "\n",
      "2018-05-29 15:29:08,969:INFO: - Eval metrics : PSNR: 26.505 ; SSIM: 0.921 ; mse_loss: 161.136\n",
      "\n",
      "2018-05-29 15:29:09,949:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 15:29:09,950:INFO: Epoch 9/100\n",
      "\n",
      "2018-05-29 15:46:33,965:INFO: - Train metrics: PSNR: 26.488 ; SSIM: 0.922 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 165.737\n",
      "\n",
      "2018-05-29 15:46:37,627:INFO: - Eval metrics : PSNR: 26.440 ; SSIM: 0.919 ; mse_loss: 163.106\n",
      "\n",
      "2018-05-29 15:46:38,161:INFO: Epoch 10/100\n",
      "\n",
      "2018-05-29 16:04:03,958:INFO: - Train metrics: PSNR: 26.484 ; SSIM: 0.921 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 161.062\n",
      "\n",
      "2018-05-29 16:04:07,591:INFO: - Eval metrics : PSNR: 26.453 ; SSIM: 0.920 ; mse_loss: 162.856\n",
      "\n",
      "2018-05-29 16:04:08,026:INFO: Epoch 11/100\n",
      "\n",
      "2018-05-29 16:21:30,689:INFO: - Train metrics: PSNR: 26.408 ; SSIM: 0.918 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 163.802\n",
      "\n",
      "2018-05-29 16:21:34,366:INFO: - Eval metrics : PSNR: 26.496 ; SSIM: 0.920 ; mse_loss: 161.434\n",
      "\n",
      "2018-05-29 16:21:34,901:INFO: Epoch 12/100\n",
      "\n",
      "2018-05-29 16:38:58,012:INFO: - Train metrics: PSNR: 26.418 ; SSIM: 0.922 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 165.956\n",
      "\n",
      "2018-05-29 16:39:01,641:INFO: - Eval metrics : PSNR: 26.549 ; SSIM: 0.922 ; mse_loss: 159.771\n",
      "\n",
      "2018-05-29 16:39:02,643:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 16:39:02,644:INFO: Epoch 13/100\n",
      "\n",
      "2018-05-29 16:56:26,324:INFO: - Train metrics: PSNR: 26.616 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 153.337\n",
      "\n",
      "2018-05-29 16:56:29,940:INFO: - Eval metrics : PSNR: 26.518 ; SSIM: 0.921 ; mse_loss: 160.589\n",
      "\n",
      "2018-05-29 16:56:30,477:INFO: Epoch 14/100\n",
      "\n",
      "2018-05-29 17:13:51,817:INFO: - Train metrics: PSNR: 26.590 ; SSIM: 0.921 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 161.429\n",
      "\n",
      "2018-05-29 17:13:55,501:INFO: - Eval metrics : PSNR: 26.506 ; SSIM: 0.921 ; mse_loss: 161.034\n",
      "\n",
      "2018-05-29 17:13:56,037:INFO: Epoch 15/100\n",
      "\n",
      "2018-05-29 17:31:18,925:INFO: - Train metrics: PSNR: 26.867 ; SSIM: 0.925 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 149.781\n",
      "\n",
      "2018-05-29 17:31:22,479:INFO: - Eval metrics : PSNR: 26.530 ; SSIM: 0.921 ; mse_loss: 160.205\n",
      "\n",
      "2018-05-29 17:31:22,914:INFO: Epoch 16/100\n",
      "\n",
      "2018-05-29 17:48:48,066:INFO: - Train metrics: PSNR: 26.591 ; SSIM: 0.926 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 167.909\n",
      "\n",
      "2018-05-29 17:48:51,677:INFO: - Eval metrics : PSNR: 26.551 ; SSIM: 0.922 ; mse_loss: 159.530\n",
      "\n",
      "2018-05-29 17:48:52,648:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 17:48:52,649:INFO: Epoch 17/100\n",
      "\n",
      "2018-05-29 17:49:12,057:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 17:49:12,265:INFO: - done.\n",
      "\n",
      "2018-05-29 17:49:19,957:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 17:49:19,957:INFO: Restoring parameters from experiments/gan_model/best_g.pth.tar\n",
      "\n",
      "2018-05-29 17:49:20,140:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 17:59:15,970:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 17:59:16,172:INFO: - done.\n",
      "\n",
      "2018-05-29 17:59:24,038:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 17:59:24,039:INFO: Restoring parameters from experiments/gan_model/best_g.pth.tar\n",
      "\n",
      "2018-05-29 17:59:24,236:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 18:16:47,870:INFO: - Train metrics: PSNR: 26.695 ; SSIM: 0.930 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 151.007\n",
      "\n",
      "2018-05-29 18:16:51,581:INFO: - Eval metrics : PSNR: 26.429 ; SSIM: 0.920 ; mse_loss: 163.373\n",
      "\n",
      "2018-05-29 18:16:52,615:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 18:16:52,616:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 18:34:15,547:INFO: - Train metrics: PSNR: 26.317 ; SSIM: 0.914 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 170.813\n",
      "\n",
      "2018-05-29 18:34:19,297:INFO: - Eval metrics : PSNR: 26.525 ; SSIM: 0.921 ; mse_loss: 160.553\n",
      "\n",
      "2018-05-29 18:34:20,334:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 18:34:20,335:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 18:42:05,593:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 18:42:05,798:INFO: - done.\n",
      "\n",
      "2018-05-29 18:42:13,206:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 18:42:13,207:INFO: Restoring parameters from experiments/gan_model/best_g.pth.tar\n",
      "\n",
      "2018-05-29 18:42:13,380:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 18:47:26,474:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 18:47:26,675:INFO: - done.\n",
      "\n",
      "2018-05-29 18:47:34,379:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 18:47:34,379:INFO: Restoring parameters from experiments/gan_model/best_g.pth.tar\n",
      "\n",
      "2018-05-29 18:47:34,569:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 19:05:00,606:INFO: - Train metrics: PSNR: 26.695 ; SSIM: 0.930 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 151.045\n",
      "\n",
      "2018-05-29 21:31:16,504:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-29 21:31:16,709:INFO: - done.\n",
      "\n",
      "2018-05-29 21:31:24,387:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-29 21:31:24,388:INFO: Restoring parameters from experiments/gan_model/best_g.pth.tar\n",
      "\n",
      "2018-05-29 21:31:24,573:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-29 21:48:47,423:INFO: - Train metrics: PSNR: 26.688 ; SSIM: 0.930 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 151.361\n",
      "\n",
      "2018-05-29 21:48:51,045:INFO: - Eval metrics : PSNR: 26.448 ; SSIM: 0.921 ; mse_loss: 162.778\n",
      "\n",
      "2018-05-29 21:48:52,023:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 21:48:52,024:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-29 22:06:15,109:INFO: - Train metrics: PSNR: 26.331 ; SSIM: 0.915 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 170.424\n",
      "\n",
      "2018-05-29 22:06:18,702:INFO: - Eval metrics : PSNR: 26.583 ; SSIM: 0.921 ; mse_loss: 158.554\n",
      "\n",
      "2018-05-29 22:06:19,672:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 22:06:19,672:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-29 22:23:43,710:INFO: - Train metrics: PSNR: 26.874 ; SSIM: 0.924 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 151.177\n",
      "\n",
      "2018-05-29 22:23:47,258:INFO: - Eval metrics : PSNR: 26.561 ; SSIM: 0.920 ; mse_loss: 159.257\n",
      "\n",
      "2018-05-29 22:23:47,793:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-29 22:41:11,405:INFO: - Train metrics: PSNR: 26.590 ; SSIM: 0.928 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 156.783\n",
      "\n",
      "2018-05-29 22:41:15,007:INFO: - Eval metrics : PSNR: 26.558 ; SSIM: 0.921 ; mse_loss: 159.299\n",
      "\n",
      "2018-05-29 22:41:15,541:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-29 22:58:39,581:INFO: - Train metrics: PSNR: 26.926 ; SSIM: 0.928 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 147.769\n",
      "\n",
      "2018-05-29 22:58:43,092:INFO: - Eval metrics : PSNR: 26.575 ; SSIM: 0.922 ; mse_loss: 158.650\n",
      "\n",
      "2018-05-29 22:58:43,624:INFO: Epoch 6/100\n",
      "\n",
      "2018-05-29 23:16:09,094:INFO: - Train metrics: PSNR: 26.989 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 144.706\n",
      "\n",
      "2018-05-29 23:16:12,626:INFO: - Eval metrics : PSNR: 26.610 ; SSIM: 0.922 ; mse_loss: 157.435\n",
      "\n",
      "2018-05-29 23:16:13,613:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-29 23:16:13,614:INFO: Epoch 7/100\n",
      "\n",
      "2018-05-29 23:33:40,702:INFO: - Train metrics: PSNR: 26.212 ; SSIM: 0.926 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 177.918\n",
      "\n",
      "2018-05-29 23:33:44,685:INFO: - Eval metrics : PSNR: 26.548 ; SSIM: 0.922 ; mse_loss: 159.348\n",
      "\n",
      "2018-05-29 23:33:45,220:INFO: Epoch 8/100\n",
      "\n",
      "2018-05-29 23:51:12,317:INFO: - Train metrics: PSNR: 26.803 ; SSIM: 0.932 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 148.806\n",
      "\n",
      "2018-05-29 23:51:15,934:INFO: - Eval metrics : PSNR: 26.588 ; SSIM: 0.922 ; mse_loss: 158.227\n",
      "\n",
      "2018-05-29 23:51:16,370:INFO: Epoch 9/100\n",
      "\n",
      "2018-05-30 00:08:44,125:INFO: - Train metrics: PSNR: 26.587 ; SSIM: 0.924 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 162.401\n",
      "\n",
      "2018-05-30 00:08:47,661:INFO: - Eval metrics : PSNR: 26.513 ; SSIM: 0.919 ; mse_loss: 160.516\n",
      "\n",
      "2018-05-30 00:08:48,197:INFO: Epoch 10/100\n",
      "\n",
      "2018-05-30 00:26:15,627:INFO: - Train metrics: PSNR: 26.584 ; SSIM: 0.923 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 158.009\n",
      "\n",
      "2018-05-30 00:26:19,169:INFO: - Eval metrics : PSNR: 26.560 ; SSIM: 0.920 ; mse_loss: 159.240\n",
      "\n",
      "2018-05-30 00:26:19,704:INFO: Epoch 11/100\n",
      "\n",
      "2018-05-30 00:43:46,224:INFO: - Train metrics: PSNR: 26.502 ; SSIM: 0.920 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 160.265\n",
      "\n",
      "2018-05-30 00:43:49,835:INFO: - Eval metrics : PSNR: 26.558 ; SSIM: 0.922 ; mse_loss: 159.173\n",
      "\n",
      "2018-05-30 00:43:50,270:INFO: Epoch 12/100\n",
      "\n",
      "2018-05-30 01:01:16,529:INFO: - Train metrics: PSNR: 26.422 ; SSIM: 0.923 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 165.498\n",
      "\n",
      "2018-05-30 01:01:20,146:INFO: - Eval metrics : PSNR: 26.606 ; SSIM: 0.923 ; mse_loss: 157.453\n",
      "\n",
      "2018-05-30 01:01:20,648:INFO: Epoch 13/100\n",
      "\n",
      "2018-05-30 04:10:28,934:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-30 04:10:29,202:INFO: - done.\n",
      "\n",
      "2018-05-30 04:10:39,257:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-30 04:10:39,257:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-30 04:24:16,997:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-30 04:24:16,997:INFO: - done.\n",
      "\n",
      "2018-05-30 04:24:27,089:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-30 04:24:27,090:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-30 04:24:29,167:INFO: - Train metrics: PSNR: 11.064 ; SSIM: 0.350 ; g_loss: 0.084 ; d_loss: 0.993 ; mse_loss: 5256.681\n",
      "\n",
      "2018-05-30 04:24:29,988:INFO: - Eval metrics : PSNR: 11.206 ; SSIM: 0.417 ; mse_loss: 5206.057\n",
      "\n",
      "2018-05-30 04:24:30,979:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:24:30,980:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-30 04:24:32,918:INFO: - Train metrics: PSNR: 11.350 ; SSIM: 0.428 ; g_loss: 0.079 ; d_loss: 0.979 ; mse_loss: 4923.144\n",
      "\n",
      "2018-05-30 04:24:33,732:INFO: - Eval metrics : PSNR: 11.247 ; SSIM: 0.443 ; mse_loss: 5163.270\n",
      "\n",
      "2018-05-30 04:24:34,797:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:24:34,797:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-30 04:24:36,747:INFO: - Train metrics: PSNR: 11.691 ; SSIM: 0.494 ; g_loss: 0.073 ; d_loss: 0.963 ; mse_loss: 4553.335\n",
      "\n",
      "2018-05-30 04:24:37,560:INFO: - Eval metrics : PSNR: 11.287 ; SSIM: 0.465 ; mse_loss: 5121.034\n",
      "\n",
      "2018-05-30 04:24:38,518:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:24:38,519:INFO: Epoch 4/100\n",
      "\n",
      "2018-05-30 04:24:40,442:INFO: - Train metrics: PSNR: 12.109 ; SSIM: 0.548 ; g_loss: 0.067 ; d_loss: 0.948 ; mse_loss: 4139.370\n",
      "\n",
      "2018-05-30 04:24:41,355:INFO: - Eval metrics : PSNR: 11.329 ; SSIM: 0.483 ; mse_loss: 5076.964\n",
      "\n",
      "2018-05-30 04:24:42,212:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:24:42,213:INFO: Epoch 5/100\n",
      "\n",
      "2018-05-30 04:24:44,175:INFO: - Train metrics: PSNR: 12.614 ; SSIM: 0.589 ; g_loss: 0.060 ; d_loss: 0.932 ; mse_loss: 3695.171\n",
      "\n",
      "2018-05-30 04:24:44,978:INFO: - Eval metrics : PSNR: 11.375 ; SSIM: 0.497 ; mse_loss: 5027.244\n",
      "\n",
      "2018-05-30 04:24:45,886:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:24:45,886:INFO: Epoch 6/100\n",
      "\n",
      "2018-05-30 04:24:47,817:INFO: - Train metrics: PSNR: 13.206 ; SSIM: 0.618 ; g_loss: 0.053 ; d_loss: 0.917 ; mse_loss: 3243.428\n",
      "\n",
      "2018-05-30 04:24:48,710:INFO: - Eval metrics : PSNR: 11.430 ; SSIM: 0.508 ; mse_loss: 4964.732\n",
      "\n",
      "2018-05-30 04:24:49,753:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:24:49,754:INFO: Epoch 7/100\n",
      "\n",
      "2018-05-30 04:24:51,771:INFO: - Train metrics: PSNR: 13.888 ; SSIM: 0.640 ; g_loss: 0.046 ; d_loss: 0.903 ; mse_loss: 2799.811\n",
      "\n",
      "2018-05-30 04:24:52,598:INFO: - Eval metrics : PSNR: 11.507 ; SSIM: 0.515 ; mse_loss: 4875.591\n",
      "\n",
      "2018-05-30 04:24:53,616:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:24:53,617:INFO: Epoch 8/100\n",
      "\n",
      "2018-05-30 04:24:55,567:INFO: - Train metrics: PSNR: 14.673 ; SSIM: 0.657 ; g_loss: 0.040 ; d_loss: 0.890 ; mse_loss: 2371.194\n",
      "\n",
      "2018-05-30 04:24:56,372:INFO: - Eval metrics : PSNR: 11.623 ; SSIM: 0.520 ; mse_loss: 4737.572\n",
      "\n",
      "2018-05-30 04:24:57,322:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:24:57,323:INFO: Epoch 9/100\n",
      "\n",
      "2018-05-30 04:24:59,283:INFO: - Train metrics: PSNR: 15.539 ; SSIM: 0.676 ; g_loss: 0.034 ; d_loss: 0.880 ; mse_loss: 1979.977\n",
      "\n",
      "2018-05-30 04:25:00,112:INFO: - Eval metrics : PSNR: 11.809 ; SSIM: 0.523 ; mse_loss: 4520.147\n",
      "\n",
      "2018-05-30 04:25:01,063:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:01,064:INFO: Epoch 10/100\n",
      "\n",
      "2018-05-30 04:25:03,012:INFO: - Train metrics: PSNR: 16.334 ; SSIM: 0.700 ; g_loss: 0.029 ; d_loss: 0.870 ; mse_loss: 1673.602\n",
      "\n",
      "2018-05-30 04:25:03,808:INFO: - Eval metrics : PSNR: 12.107 ; SSIM: 0.525 ; mse_loss: 4192.138\n",
      "\n",
      "2018-05-30 04:25:04,841:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:04,858:INFO: Epoch 11/100\n",
      "\n",
      "2018-05-30 04:25:06,822:INFO: - Train metrics: PSNR: 16.841 ; SSIM: 0.721 ; g_loss: 0.026 ; d_loss: 0.862 ; mse_loss: 1482.890\n",
      "\n",
      "2018-05-30 04:25:07,633:INFO: - Eval metrics : PSNR: 12.561 ; SSIM: 0.528 ; mse_loss: 3744.012\n",
      "\n",
      "2018-05-30 04:25:08,590:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:08,591:INFO: Epoch 12/100\n",
      "\n",
      "2018-05-30 04:25:10,537:INFO: - Train metrics: PSNR: 17.055 ; SSIM: 0.721 ; g_loss: 0.025 ; d_loss: 0.849 ; mse_loss: 1382.127\n",
      "\n",
      "2018-05-30 04:25:11,373:INFO: - Eval metrics : PSNR: 13.197 ; SSIM: 0.533 ; mse_loss: 3212.288\n",
      "\n",
      "2018-05-30 04:25:12,311:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:12,311:INFO: Epoch 13/100\n",
      "\n",
      "2018-05-30 04:25:14,260:INFO: - Train metrics: PSNR: 17.167 ; SSIM: 0.707 ; g_loss: 0.024 ; d_loss: 0.842 ; mse_loss: 1315.990\n",
      "\n",
      "2018-05-30 04:25:15,079:INFO: - Eval metrics : PSNR: 14.000 ; SSIM: 0.543 ; mse_loss: 2672.615\n",
      "\n",
      "2018-05-30 04:25:16,061:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:16,131:INFO: Epoch 14/100\n",
      "\n",
      "2018-05-30 04:25:18,068:INFO: - Train metrics: PSNR: 17.321 ; SSIM: 0.697 ; g_loss: 0.023 ; d_loss: 0.827 ; mse_loss: 1252.836\n",
      "\n",
      "2018-05-30 04:25:18,866:INFO: - Eval metrics : PSNR: 14.889 ; SSIM: 0.558 ; mse_loss: 2202.506\n",
      "\n",
      "2018-05-30 04:25:19,822:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:19,823:INFO: Epoch 15/100\n",
      "\n",
      "2018-05-30 04:25:21,851:INFO: - Train metrics: PSNR: 17.529 ; SSIM: 0.695 ; g_loss: 0.022 ; d_loss: 0.830 ; mse_loss: 1194.608\n",
      "\n",
      "2018-05-30 04:25:22,742:INFO: - Eval metrics : PSNR: 15.713 ; SSIM: 0.578 ; mse_loss: 1853.557\n",
      "\n",
      "2018-05-30 04:25:23,695:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:23,696:INFO: Epoch 16/100\n",
      "\n",
      "2018-05-30 04:25:25,636:INFO: - Train metrics: PSNR: 17.743 ; SSIM: 0.700 ; g_loss: 0.021 ; d_loss: 0.806 ; mse_loss: 1146.775\n",
      "\n",
      "2018-05-30 04:25:26,443:INFO: - Eval metrics : PSNR: 16.274 ; SSIM: 0.597 ; mse_loss: 1648.542\n",
      "\n",
      "2018-05-30 04:25:27,419:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:27,419:INFO: Epoch 17/100\n",
      "\n",
      "2018-05-30 04:25:29,368:INFO: - Train metrics: PSNR: 17.937 ; SSIM: 0.709 ; g_loss: 0.020 ; d_loss: 0.808 ; mse_loss: 1099.060\n",
      "\n",
      "2018-05-30 04:25:30,174:INFO: - Eval metrics : PSNR: 16.513 ; SSIM: 0.612 ; mse_loss: 1562.576\n",
      "\n",
      "2018-05-30 04:25:31,245:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:31,245:INFO: Epoch 18/100\n",
      "\n",
      "2018-05-30 04:25:33,202:INFO: - Train metrics: PSNR: 18.119 ; SSIM: 0.721 ; g_loss: 0.020 ; d_loss: 0.792 ; mse_loss: 1044.808\n",
      "\n",
      "2018-05-30 04:25:33,996:INFO: - Eval metrics : PSNR: 16.630 ; SSIM: 0.624 ; mse_loss: 1518.257\n",
      "\n",
      "2018-05-30 04:25:34,860:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:35,057:INFO: Epoch 19/100\n",
      "\n",
      "2018-05-30 04:25:37,008:INFO: - Train metrics: PSNR: 18.287 ; SSIM: 0.730 ; g_loss: 0.019 ; d_loss: 0.774 ; mse_loss: 995.412\n",
      "\n",
      "2018-05-30 04:25:37,832:INFO: - Eval metrics : PSNR: 16.819 ; SSIM: 0.635 ; mse_loss: 1450.381\n",
      "\n",
      "2018-05-30 04:25:38,788:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:38,789:INFO: Epoch 20/100\n",
      "\n",
      "2018-05-30 04:25:40,794:INFO: - Train metrics: PSNR: 18.429 ; SSIM: 0.736 ; g_loss: 0.018 ; d_loss: 0.811 ; mse_loss: 960.105\n",
      "\n",
      "2018-05-30 04:25:41,599:INFO: - Eval metrics : PSNR: 17.087 ; SSIM: 0.645 ; mse_loss: 1357.778\n",
      "\n",
      "2018-05-30 04:25:42,512:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:42,513:INFO: Epoch 21/100\n",
      "\n",
      "2018-05-30 04:25:44,459:INFO: - Train metrics: PSNR: 18.576 ; SSIM: 0.741 ; g_loss: 0.018 ; d_loss: 0.749 ; mse_loss: 929.717\n",
      "\n",
      "2018-05-30 04:25:45,259:INFO: - Eval metrics : PSNR: 17.367 ; SSIM: 0.653 ; mse_loss: 1264.320\n",
      "\n",
      "2018-05-30 04:25:46,179:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:46,180:INFO: Epoch 22/100\n",
      "\n",
      "2018-05-30 04:25:48,130:INFO: - Train metrics: PSNR: 18.755 ; SSIM: 0.747 ; g_loss: 0.017 ; d_loss: 0.757 ; mse_loss: 894.951\n",
      "\n",
      "2018-05-30 04:25:48,933:INFO: - Eval metrics : PSNR: 17.633 ; SSIM: 0.659 ; mse_loss: 1181.874\n",
      "\n",
      "2018-05-30 04:25:49,853:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:49,854:INFO: Epoch 23/100\n",
      "\n",
      "2018-05-30 04:25:51,766:INFO: - Train metrics: PSNR: 18.944 ; SSIM: 0.754 ; g_loss: 0.017 ; d_loss: 0.759 ; mse_loss: 858.623\n",
      "\n",
      "2018-05-30 04:25:52,562:INFO: - Eval metrics : PSNR: 17.874 ; SSIM: 0.662 ; mse_loss: 1114.190\n",
      "\n",
      "2018-05-30 04:25:53,470:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:53,471:INFO: Epoch 24/100\n",
      "\n",
      "2018-05-30 04:25:55,394:INFO: - Train metrics: PSNR: 19.106 ; SSIM: 0.762 ; g_loss: 0.016 ; d_loss: 0.713 ; mse_loss: 827.721\n",
      "\n",
      "2018-05-30 04:25:56,193:INFO: - Eval metrics : PSNR: 18.074 ; SSIM: 0.663 ; mse_loss: 1063.179\n",
      "\n",
      "2018-05-30 04:25:57,106:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:25:57,107:INFO: Epoch 25/100\n",
      "\n",
      "2018-05-30 04:25:59,090:INFO: - Train metrics: PSNR: 19.226 ; SSIM: 0.770 ; g_loss: 0.016 ; d_loss: 0.700 ; mse_loss: 804.410\n",
      "\n",
      "2018-05-30 04:25:59,989:INFO: - Eval metrics : PSNR: 18.221 ; SSIM: 0.661 ; mse_loss: 1028.219\n",
      "\n",
      "2018-05-30 04:26:01,022:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:01,023:INFO: Epoch 26/100\n",
      "\n",
      "2018-05-30 04:26:02,987:INFO: - Train metrics: PSNR: 19.323 ; SSIM: 0.777 ; g_loss: 0.016 ; d_loss: 0.708 ; mse_loss: 785.184\n",
      "\n",
      "2018-05-30 04:26:03,759:INFO: - Eval metrics : PSNR: 18.324 ; SSIM: 0.657 ; mse_loss: 1004.710\n",
      "\n",
      "2018-05-30 04:26:04,608:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:04,808:INFO: Epoch 27/100\n",
      "\n",
      "2018-05-30 04:26:06,734:INFO: - Train metrics: PSNR: 19.424 ; SSIM: 0.784 ; g_loss: 0.015 ; d_loss: 0.686 ; mse_loss: 765.426\n",
      "\n",
      "2018-05-30 04:26:07,536:INFO: - Eval metrics : PSNR: 18.402 ; SSIM: 0.653 ; mse_loss: 986.889\n",
      "\n",
      "2018-05-30 04:26:08,478:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:08,479:INFO: Epoch 28/100\n",
      "\n",
      "2018-05-30 04:26:10,417:INFO: - Train metrics: PSNR: 19.539 ; SSIM: 0.790 ; g_loss: 0.015 ; d_loss: 0.652 ; mse_loss: 743.738\n",
      "\n",
      "2018-05-30 04:26:11,210:INFO: - Eval metrics : PSNR: 18.469 ; SSIM: 0.649 ; mse_loss: 971.085\n",
      "\n",
      "2018-05-30 04:26:12,253:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:12,253:INFO: Epoch 29/100\n",
      "\n",
      "2018-05-30 04:26:14,196:INFO: - Train metrics: PSNR: 19.661 ; SSIM: 0.794 ; g_loss: 0.015 ; d_loss: 0.641 ; mse_loss: 721.885\n",
      "\n",
      "2018-05-30 04:26:15,001:INFO: - Eval metrics : PSNR: 18.533 ; SSIM: 0.646 ; mse_loss: 955.784\n",
      "\n",
      "2018-05-30 04:26:16,057:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:16,058:INFO: Epoch 30/100\n",
      "\n",
      "2018-05-30 04:26:18,006:INFO: - Train metrics: PSNR: 19.779 ; SSIM: 0.798 ; g_loss: 0.014 ; d_loss: 0.631 ; mse_loss: 701.604\n",
      "\n",
      "2018-05-30 04:26:18,794:INFO: - Eval metrics : PSNR: 18.604 ; SSIM: 0.644 ; mse_loss: 939.518\n",
      "\n",
      "2018-05-30 04:26:19,740:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:19,740:INFO: Epoch 31/100\n",
      "\n",
      "2018-05-30 04:26:21,685:INFO: - Train metrics: PSNR: 19.896 ; SSIM: 0.802 ; g_loss: 0.014 ; d_loss: 0.611 ; mse_loss: 682.460\n",
      "\n",
      "2018-05-30 04:26:22,476:INFO: - Eval metrics : PSNR: 18.696 ; SSIM: 0.645 ; mse_loss: 920.286\n",
      "\n",
      "2018-05-30 04:26:23,492:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:23,493:INFO: Epoch 32/100\n",
      "\n",
      "2018-05-30 04:26:25,465:INFO: - Train metrics: PSNR: 20.019 ; SSIM: 0.808 ; g_loss: 0.014 ; d_loss: 0.595 ; mse_loss: 663.130\n",
      "\n",
      "2018-05-30 04:26:26,263:INFO: - Eval metrics : PSNR: 18.807 ; SSIM: 0.647 ; mse_loss: 898.466\n",
      "\n",
      "2018-05-30 04:26:27,175:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:27,176:INFO: Epoch 33/100\n",
      "\n",
      "2018-05-30 04:26:29,106:INFO: - Train metrics: PSNR: 20.146 ; SSIM: 0.813 ; g_loss: 0.013 ; d_loss: 0.574 ; mse_loss: 643.989\n",
      "\n",
      "2018-05-30 04:26:29,909:INFO: - Eval metrics : PSNR: 18.912 ; SSIM: 0.651 ; mse_loss: 879.076\n",
      "\n",
      "2018-05-30 04:26:30,846:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:30,846:INFO: Epoch 34/100\n",
      "\n",
      "2018-05-30 04:26:32,784:INFO: - Train metrics: PSNR: 20.261 ; SSIM: 0.819 ; g_loss: 0.013 ; d_loss: 0.559 ; mse_loss: 627.294\n",
      "\n",
      "2018-05-30 04:26:33,578:INFO: - Eval metrics : PSNR: 18.982 ; SSIM: 0.654 ; mse_loss: 867.879\n",
      "\n",
      "2018-05-30 04:26:34,497:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:34,498:INFO: Epoch 35/100\n",
      "\n",
      "2018-05-30 04:26:36,435:INFO: - Train metrics: PSNR: 20.348 ; SSIM: 0.823 ; g_loss: 0.013 ; d_loss: 0.544 ; mse_loss: 615.126\n",
      "\n",
      "2018-05-30 04:26:37,226:INFO: - Eval metrics : PSNR: 19.008 ; SSIM: 0.657 ; mse_loss: 865.990\n",
      "\n",
      "2018-05-30 04:26:38,133:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:38,133:INFO: Epoch 36/100\n",
      "\n",
      "2018-05-30 04:26:40,060:INFO: - Train metrics: PSNR: 20.407 ; SSIM: 0.826 ; g_loss: 0.013 ; d_loss: 0.528 ; mse_loss: 607.216\n",
      "\n",
      "2018-05-30 04:26:40,881:INFO: - Eval metrics : PSNR: 19.007 ; SSIM: 0.660 ; mse_loss: 869.198\n",
      "\n",
      "2018-05-30 04:26:41,362:INFO: Epoch 37/100\n",
      "\n",
      "2018-05-30 04:26:43,297:INFO: - Train metrics: PSNR: 20.457 ; SSIM: 0.828 ; g_loss: 0.013 ; d_loss: 0.509 ; mse_loss: 600.709\n",
      "\n",
      "2018-05-30 04:26:44,097:INFO: - Eval metrics : PSNR: 19.009 ; SSIM: 0.663 ; mse_loss: 871.224\n",
      "\n",
      "2018-05-30 04:26:45,027:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:45,028:INFO: Epoch 38/100\n",
      "\n",
      "2018-05-30 04:26:46,964:INFO: - Train metrics: PSNR: 20.521 ; SSIM: 0.831 ; g_loss: 0.013 ; d_loss: 0.491 ; mse_loss: 592.334\n",
      "\n",
      "2018-05-30 04:26:47,764:INFO: - Eval metrics : PSNR: 19.036 ; SSIM: 0.667 ; mse_loss: 867.378\n",
      "\n",
      "2018-05-30 04:26:48,663:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:48,664:INFO: Epoch 39/100\n",
      "\n",
      "2018-05-30 04:26:50,635:INFO: - Train metrics: PSNR: 20.606 ; SSIM: 0.833 ; g_loss: 0.012 ; d_loss: 0.475 ; mse_loss: 581.292\n",
      "\n",
      "2018-05-30 04:26:51,377:INFO: - Eval metrics : PSNR: 19.097 ; SSIM: 0.671 ; mse_loss: 855.730\n",
      "\n",
      "2018-05-30 04:26:52,327:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:52,423:INFO: Epoch 40/100\n",
      "\n",
      "2018-05-30 04:26:54,340:INFO: - Train metrics: PSNR: 20.698 ; SSIM: 0.836 ; g_loss: 0.012 ; d_loss: 0.459 ; mse_loss: 569.360\n",
      "\n",
      "2018-05-30 04:26:55,099:INFO: - Eval metrics : PSNR: 19.187 ; SSIM: 0.675 ; mse_loss: 837.273\n",
      "\n",
      "2018-05-30 04:26:56,123:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:56,124:INFO: Epoch 41/100\n",
      "\n",
      "2018-05-30 04:26:58,061:INFO: - Train metrics: PSNR: 20.782 ; SSIM: 0.837 ; g_loss: 0.012 ; d_loss: 0.441 ; mse_loss: 558.594\n",
      "\n",
      "2018-05-30 04:26:58,813:INFO: - Eval metrics : PSNR: 19.291 ; SSIM: 0.679 ; mse_loss: 815.631\n",
      "\n",
      "2018-05-30 04:26:59,750:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:26:59,751:INFO: Epoch 42/100\n",
      "\n",
      "2018-05-30 04:27:01,705:INFO: - Train metrics: PSNR: 20.854 ; SSIM: 0.839 ; g_loss: 0.012 ; d_loss: 0.424 ; mse_loss: 549.517\n",
      "\n",
      "2018-05-30 04:27:02,477:INFO: - Eval metrics : PSNR: 19.389 ; SSIM: 0.682 ; mse_loss: 795.313\n",
      "\n",
      "2018-05-30 04:27:03,341:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:27:03,341:INFO: Epoch 43/100\n",
      "\n",
      "2018-05-30 04:27:05,296:INFO: - Train metrics: PSNR: 20.920 ; SSIM: 0.840 ; g_loss: 0.012 ; d_loss: 0.407 ; mse_loss: 541.371\n",
      "\n",
      "2018-05-30 04:27:06,106:INFO: - Eval metrics : PSNR: 19.470 ; SSIM: 0.685 ; mse_loss: 778.558\n",
      "\n",
      "2018-05-30 04:27:07,145:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:27:07,145:INFO: Epoch 44/100\n",
      "\n",
      "2018-05-30 04:27:09,110:INFO: - Train metrics: PSNR: 20.986 ; SSIM: 0.841 ; g_loss: 0.012 ; d_loss: 0.392 ; mse_loss: 533.348\n",
      "\n",
      "2018-05-30 04:27:09,920:INFO: - Eval metrics : PSNR: 19.544 ; SSIM: 0.690 ; mse_loss: 764.026\n",
      "\n",
      "2018-05-30 04:27:10,950:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:27:10,951:INFO: Epoch 45/100\n",
      "\n",
      "2018-05-30 04:27:12,900:INFO: - Train metrics: PSNR: 21.053 ; SSIM: 0.844 ; g_loss: 0.012 ; d_loss: 0.376 ; mse_loss: 525.398\n",
      "\n",
      "2018-05-30 04:27:13,707:INFO: - Eval metrics : PSNR: 19.621 ; SSIM: 0.696 ; mse_loss: 749.950\n",
      "\n",
      "2018-05-30 04:27:14,652:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:27:14,653:INFO: Epoch 46/100\n",
      "\n",
      "2018-05-30 04:27:16,614:INFO: - Train metrics: PSNR: 21.117 ; SSIM: 0.846 ; g_loss: 0.012 ; d_loss: 0.358 ; mse_loss: 517.883\n",
      "\n",
      "2018-05-30 04:27:17,442:INFO: - Eval metrics : PSNR: 19.697 ; SSIM: 0.702 ; mse_loss: 736.468\n",
      "\n",
      "2018-05-30 04:27:18,391:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:27:18,392:INFO: Epoch 47/100\n",
      "\n",
      "2018-05-30 04:27:20,349:INFO: - Train metrics: PSNR: 21.182 ; SSIM: 0.849 ; g_loss: 0.011 ; d_loss: 0.342 ; mse_loss: 510.468\n",
      "\n",
      "2018-05-30 04:27:21,130:INFO: - Eval metrics : PSNR: 19.771 ; SSIM: 0.709 ; mse_loss: 723.287\n",
      "\n",
      "2018-05-30 04:27:22,085:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:27:22,085:INFO: Epoch 48/100\n",
      "\n",
      "2018-05-30 04:27:24,054:INFO: - Train metrics: PSNR: 21.252 ; SSIM: 0.851 ; g_loss: 0.011 ; d_loss: 0.328 ; mse_loss: 502.547\n",
      "\n",
      "2018-05-30 04:27:24,875:INFO: - Eval metrics : PSNR: 19.846 ; SSIM: 0.715 ; mse_loss: 709.436\n",
      "\n",
      "2018-05-30 04:27:25,894:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:27:25,894:INFO: Epoch 49/100\n",
      "\n",
      "2018-05-30 04:27:27,857:INFO: - Train metrics: PSNR: 21.323 ; SSIM: 0.854 ; g_loss: 0.011 ; d_loss: 0.313 ; mse_loss: 494.567\n",
      "\n",
      "2018-05-30 04:27:28,677:INFO: - Eval metrics : PSNR: 19.918 ; SSIM: 0.720 ; mse_loss: 696.040\n",
      "\n",
      "2018-05-30 04:27:29,607:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 04:27:29,607:INFO: Epoch 50/100\n",
      "\n",
      "2018-05-30 04:27:31,562:INFO: - Train metrics: PSNR: 21.385 ; SSIM: 0.855 ; g_loss: 0.011 ; d_loss: 0.297 ; mse_loss: 487.670\n",
      "\n",
      "2018-05-30 04:27:32,385:INFO: - Eval metrics : PSNR: 19.976 ; SSIM: 0.724 ; mse_loss: 685.332\n",
      "\n",
      "2018-05-30 05:55:30,577:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-30 05:55:31,783:INFO: - done.\n",
      "\n",
      "2018-05-30 05:56:00,781:INFO: Starting training for 100 epoch(s)\n",
      "\n",
      "2018-05-30 05:56:00,781:INFO: Restoring parameters from experiments/gan_model/best_g.pth.tar\n",
      "\n",
      "2018-05-30 05:56:03,059:INFO: Epoch 1/100\n",
      "\n",
      "2018-05-30 06:13:27,267:INFO: - Train metrics: PSNR: 23.716 ; SSIM: 0.869 ; g_loss: 0.008 ; d_loss: 0.133 ; mse_loss: 358.264\n",
      "\n",
      "2018-05-30 06:13:31,186:INFO: - Eval metrics : PSNR: 25.288 ; SSIM: 0.899 ; mse_loss: 208.770\n",
      "\n",
      "2018-05-30 06:13:32,291:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 06:13:32,314:INFO: Epoch 2/100\n",
      "\n",
      "2018-05-30 06:32:01,978:INFO: - Train metrics: PSNR: 25.251 ; SSIM: 0.899 ; g_loss: 0.006 ; d_loss: 0.000 ; mse_loss: 215.234\n",
      "\n",
      "2018-05-30 06:32:08,382:INFO: - Eval metrics : PSNR: 25.759 ; SSIM: 0.908 ; mse_loss: 188.538\n",
      "\n",
      "2018-05-30 06:32:09,577:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 06:32:09,586:INFO: Epoch 3/100\n",
      "\n",
      "2018-05-30 06:33:46,545:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-30 06:33:46,914:INFO: - done.\n",
      "\n",
      "2018-05-30 06:34:12,608:INFO: Starting training for 50 epoch(s)\n",
      "\n",
      "2018-05-30 06:34:12,609:INFO: Restoring parameters from experiments/gan_model/best_g.pth.tar\n",
      "\n",
      "2018-05-30 06:34:12,980:INFO: Epoch 1/50\n",
      "\n",
      "2018-05-30 06:53:18,291:INFO: - Train metrics: PSNR: 25.764 ; SSIM: 0.916 ; g_loss: 0.005 ; d_loss: 0.758 ; mse_loss: 185.973\n",
      "\n",
      "2018-05-30 06:53:24,338:INFO: - Eval metrics : PSNR: 25.874 ; SSIM: 0.913 ; mse_loss: 184.179\n",
      "\n",
      "2018-05-30 06:53:25,494:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 06:53:25,502:INFO: Epoch 2/50\n",
      "\n",
      "2018-05-30 06:58:32,020:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-30 06:58:32,344:INFO: - done.\n",
      "\n",
      "2018-05-30 06:58:54,841:INFO: Starting training for 50 epoch(s)\n",
      "\n",
      "2018-05-30 06:58:54,842:INFO: Restoring parameters from experiments/gan_model/best_g.pth.tar\n",
      "\n",
      "2018-05-30 06:58:55,198:INFO: Epoch 1/50\n",
      "\n",
      "2018-05-30 07:17:00,931:INFO: - Train metrics: PSNR: 25.990 ; SSIM: 0.921 ; g_loss: 0.005 ; d_loss: 1.016 ; mse_loss: 176.635\n",
      "\n",
      "2018-05-30 07:17:06,502:INFO: - Eval metrics : PSNR: 26.009 ; SSIM: 0.913 ; mse_loss: 178.719\n",
      "\n",
      "2018-05-30 07:17:07,623:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 07:17:07,624:INFO: Epoch 2/50\n",
      "\n",
      "2018-05-30 07:35:08,004:INFO: - Train metrics: PSNR: 25.825 ; SSIM: 0.907 ; g_loss: 0.004 ; d_loss: 1.006 ; mse_loss: 189.931\n",
      "\n",
      "2018-05-30 07:35:13,488:INFO: - Eval metrics : PSNR: 26.105 ; SSIM: 0.914 ; mse_loss: 175.395\n",
      "\n",
      "2018-05-30 07:35:14,614:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 07:35:14,615:INFO: Epoch 3/50\n",
      "\n",
      "2018-05-30 07:53:15,280:INFO: - Train metrics: PSNR: 26.374 ; SSIM: 0.914 ; g_loss: 0.004 ; d_loss: 1.017 ; mse_loss: 168.442\n",
      "\n",
      "2018-05-30 07:53:20,544:INFO: - Eval metrics : PSNR: 26.190 ; SSIM: 0.916 ; mse_loss: 172.288\n",
      "\n",
      "2018-05-30 07:53:21,593:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 07:53:21,594:INFO: Epoch 4/50\n",
      "\n",
      "2018-05-30 08:11:24,526:INFO: - Train metrics: PSNR: 26.230 ; SSIM: 0.923 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 170.118\n",
      "\n",
      "2018-05-30 08:11:29,986:INFO: - Eval metrics : PSNR: 26.221 ; SSIM: 0.916 ; mse_loss: 170.947\n",
      "\n",
      "2018-05-30 08:11:31,083:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 08:11:31,083:INFO: Epoch 5/50\n",
      "\n",
      "2018-05-30 08:30:32,494:INFO: - Train metrics: PSNR: 26.598 ; SSIM: 0.923 ; g_loss: 0.004 ; d_loss: 1.003 ; mse_loss: 159.078\n",
      "\n",
      "2018-05-30 08:30:39,358:INFO: - Eval metrics : PSNR: 26.321 ; SSIM: 0.918 ; mse_loss: 167.404\n",
      "\n",
      "2018-05-30 08:30:40,536:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 08:30:40,537:INFO: Epoch 6/50\n",
      "\n",
      "2018-05-30 08:50:08,782:INFO: - Train metrics: PSNR: 26.653 ; SSIM: 0.923 ; g_loss: 0.003 ; d_loss: 1.004 ; mse_loss: 155.344\n",
      "\n",
      "2018-05-30 08:50:15,402:INFO: - Eval metrics : PSNR: 26.312 ; SSIM: 0.918 ; mse_loss: 167.909\n",
      "\n",
      "2018-05-30 08:50:16,063:INFO: Epoch 7/50\n",
      "\n",
      "2018-05-30 09:09:45,322:INFO: - Train metrics: PSNR: 25.937 ; SSIM: 0.922 ; g_loss: 0.004 ; d_loss: 1.004 ; mse_loss: 188.837\n",
      "\n",
      "2018-05-30 09:09:51,998:INFO: - Eval metrics : PSNR: 26.343 ; SSIM: 0.919 ; mse_loss: 166.818\n",
      "\n",
      "2018-05-30 09:09:53,291:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 09:09:53,292:INFO: Epoch 8/50\n",
      "\n",
      "2018-05-30 09:29:22,114:INFO: - Train metrics: PSNR: 26.501 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 1.011 ; mse_loss: 158.989\n",
      "\n",
      "2018-05-30 09:29:28,919:INFO: - Eval metrics : PSNR: 26.308 ; SSIM: 0.918 ; mse_loss: 167.879\n",
      "\n",
      "2018-05-30 09:29:29,510:INFO: Epoch 9/50\n",
      "\n",
      "2018-05-30 09:48:57,951:INFO: - Train metrics: PSNR: 26.275 ; SSIM: 0.919 ; g_loss: 0.004 ; d_loss: 1.005 ; mse_loss: 173.391\n",
      "\n",
      "2018-05-30 09:49:04,241:INFO: - Eval metrics : PSNR: 26.035 ; SSIM: 0.913 ; mse_loss: 176.846\n",
      "\n",
      "2018-05-30 09:49:04,910:INFO: Epoch 10/50\n",
      "\n",
      "2018-05-30 10:08:33,794:INFO: - Train metrics: PSNR: 26.298 ; SSIM: 0.917 ; g_loss: 0.004 ; d_loss: 1.024 ; mse_loss: 167.965\n",
      "\n",
      "2018-05-30 10:08:41,243:INFO: - Eval metrics : PSNR: 26.415 ; SSIM: 0.920 ; mse_loss: 164.402\n",
      "\n",
      "2018-05-30 10:08:42,306:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 10:08:42,306:INFO: Epoch 11/50\n",
      "\n",
      "2018-05-30 10:28:14,079:INFO: - Train metrics: PSNR: 26.253 ; SSIM: 0.913 ; g_loss: 0.004 ; d_loss: 0.791 ; mse_loss: 168.935\n",
      "\n",
      "2018-05-30 10:28:20,497:INFO: - Eval metrics : PSNR: 26.093 ; SSIM: 0.902 ; mse_loss: 175.018\n",
      "\n",
      "2018-05-30 10:28:21,094:INFO: Epoch 12/50\n",
      "\n",
      "2018-05-30 10:47:51,504:INFO: - Train metrics: PSNR: 26.278 ; SSIM: 0.920 ; g_loss: 0.005 ; d_loss: 0.055 ; mse_loss: 171.153\n",
      "\n",
      "2018-05-30 10:47:58,451:INFO: - Eval metrics : PSNR: 26.466 ; SSIM: 0.920 ; mse_loss: 162.588\n",
      "\n",
      "2018-05-30 10:47:59,586:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 10:47:59,587:INFO: Epoch 13/50\n",
      "\n",
      "2018-05-30 11:07:31,678:INFO: - Train metrics: PSNR: 26.478 ; SSIM: 0.924 ; g_loss: 0.004 ; d_loss: 0.065 ; mse_loss: 157.853\n",
      "\n",
      "2018-05-30 11:07:38,116:INFO: - Eval metrics : PSNR: 26.449 ; SSIM: 0.921 ; mse_loss: 163.047\n",
      "\n",
      "2018-05-30 11:07:38,657:INFO: Epoch 14/50\n",
      "\n",
      "2018-05-30 11:27:10,684:INFO: - Train metrics: PSNR: 26.485 ; SSIM: 0.921 ; g_loss: 0.005 ; d_loss: 0.000 ; mse_loss: 165.332\n",
      "\n",
      "2018-05-30 11:27:17,740:INFO: - Eval metrics : PSNR: 26.460 ; SSIM: 0.921 ; mse_loss: 162.836\n",
      "\n",
      "2018-05-30 11:27:18,330:INFO: Epoch 15/50\n",
      "\n",
      "2018-05-30 11:46:47,230:INFO: - Train metrics: PSNR: 26.823 ; SSIM: 0.924 ; g_loss: 0.004 ; d_loss: 0.000 ; mse_loss: 151.324\n",
      "\n",
      "2018-05-30 11:46:53,499:INFO: - Eval metrics : PSNR: 26.515 ; SSIM: 0.921 ; mse_loss: 160.918\n",
      "\n",
      "2018-05-30 11:46:54,641:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 11:46:54,642:INFO: Epoch 16/50\n",
      "\n",
      "2018-05-30 12:06:24,479:INFO: - Train metrics: PSNR: 26.553 ; SSIM: 0.926 ; g_loss: 0.005 ; d_loss: 0.000 ; mse_loss: 169.609\n",
      "\n",
      "2018-05-30 12:06:31,995:INFO: - Eval metrics : PSNR: 26.506 ; SSIM: 0.921 ; mse_loss: 161.355\n",
      "\n",
      "2018-05-30 12:06:32,603:INFO: Epoch 17/50\n",
      "\n",
      "2018-05-30 12:26:03,575:INFO: - Train metrics: PSNR: 26.632 ; SSIM: 0.923 ; g_loss: 0.004 ; d_loss: 0.000 ; mse_loss: 161.616\n",
      "\n",
      "2018-05-30 12:26:10,370:INFO: - Eval metrics : PSNR: 26.526 ; SSIM: 0.922 ; mse_loss: 160.665\n",
      "\n",
      "2018-05-30 12:26:11,487:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 12:26:11,488:INFO: Epoch 18/50\n",
      "\n",
      "2018-05-30 12:45:40,838:INFO: - Train metrics: PSNR: 26.634 ; SSIM: 0.919 ; g_loss: 0.005 ; d_loss: 0.000 ; mse_loss: 161.080\n",
      "\n",
      "2018-05-30 12:45:48,045:INFO: - Eval metrics : PSNR: 26.506 ; SSIM: 0.921 ; mse_loss: 161.154\n",
      "\n",
      "2018-05-30 12:45:48,676:INFO: Epoch 19/50\n",
      "\n",
      "2018-05-30 13:05:18,158:INFO: - Train metrics: PSNR: 27.133 ; SSIM: 0.929 ; g_loss: 0.004 ; d_loss: 0.000 ; mse_loss: 141.877\n",
      "\n",
      "2018-05-30 13:05:25,225:INFO: - Eval metrics : PSNR: 26.505 ; SSIM: 0.920 ; mse_loss: 161.331\n",
      "\n",
      "2018-05-30 13:05:25,824:INFO: Epoch 20/50\n",
      "\n",
      "2018-05-30 13:24:57,779:INFO: - Train metrics: PSNR: 26.914 ; SSIM: 0.925 ; g_loss: 0.004 ; d_loss: 0.000 ; mse_loss: 142.682\n",
      "\n",
      "2018-05-30 13:25:04,722:INFO: - Eval metrics : PSNR: 26.525 ; SSIM: 0.921 ; mse_loss: 160.638\n",
      "\n",
      "2018-05-30 13:25:05,331:INFO: Epoch 21/50\n",
      "\n",
      "2018-05-30 13:44:35,714:INFO: - Train metrics: PSNR: 27.105 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 0.000 ; mse_loss: 146.778\n",
      "\n",
      "2018-05-30 13:44:42,546:INFO: - Eval metrics : PSNR: 26.548 ; SSIM: 0.922 ; mse_loss: 159.865\n",
      "\n",
      "2018-05-30 13:44:43,608:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 13:44:43,609:INFO: Epoch 22/50\n",
      "\n",
      "2018-05-30 14:04:15,746:INFO: - Train metrics: PSNR: 26.825 ; SSIM: 0.928 ; g_loss: 0.004 ; d_loss: 0.250 ; mse_loss: 154.777\n",
      "\n",
      "2018-05-30 14:04:22,154:INFO: - Eval metrics : PSNR: 26.537 ; SSIM: 0.921 ; mse_loss: 160.131\n",
      "\n",
      "2018-05-30 14:04:22,830:INFO: Epoch 23/50\n",
      "\n",
      "2018-05-30 14:23:54,658:INFO: - Train metrics: PSNR: 26.755 ; SSIM: 0.924 ; g_loss: 0.003 ; d_loss: 1.000 ; mse_loss: 154.333\n",
      "\n",
      "2018-05-30 14:24:01,990:INFO: - Eval metrics : PSNR: 26.541 ; SSIM: 0.922 ; mse_loss: 160.047\n",
      "\n",
      "2018-05-30 14:24:02,685:INFO: Epoch 24/50\n",
      "\n",
      "2018-05-30 14:43:31,915:INFO: - Train metrics: PSNR: 26.382 ; SSIM: 0.929 ; g_loss: 0.004 ; d_loss: 0.499 ; mse_loss: 160.692\n",
      "\n",
      "2018-05-30 14:43:38,766:INFO: - Eval metrics : PSNR: 26.537 ; SSIM: 0.921 ; mse_loss: 160.173\n",
      "\n",
      "2018-05-30 14:43:39,386:INFO: Epoch 25/50\n",
      "\n",
      "2018-05-30 15:03:10,218:INFO: - Train metrics: PSNR: 26.607 ; SSIM: 0.923 ; g_loss: 0.004 ; d_loss: 0.000 ; mse_loss: 160.967\n",
      "\n",
      "2018-05-30 15:03:17,114:INFO: - Eval metrics : PSNR: 26.568 ; SSIM: 0.922 ; mse_loss: 159.289\n",
      "\n",
      "2018-05-30 15:03:18,186:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 15:03:18,187:INFO: Epoch 26/50\n",
      "\n",
      "2018-05-30 15:22:02,637:INFO: - Train metrics: PSNR: 26.924 ; SSIM: 0.924 ; g_loss: 0.004 ; d_loss: 0.000 ; mse_loss: 145.209\n",
      "\n",
      "2018-05-30 15:22:08,578:INFO: - Eval metrics : PSNR: 26.565 ; SSIM: 0.922 ; mse_loss: 159.272\n",
      "\n",
      "2018-05-30 15:22:09,254:INFO: Epoch 27/50\n",
      "\n",
      "2018-05-30 15:40:33,031:INFO: - Train metrics: PSNR: 26.729 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 0.500 ; mse_loss: 148.624\n",
      "\n",
      "2018-05-30 15:40:38,883:INFO: - Eval metrics : PSNR: 26.561 ; SSIM: 0.922 ; mse_loss: 159.349\n",
      "\n",
      "2018-05-30 15:40:39,484:INFO: Epoch 28/50\n",
      "\n",
      "2018-05-30 15:59:04,135:INFO: - Train metrics: PSNR: 26.647 ; SSIM: 0.928 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 156.309\n",
      "\n",
      "2018-05-30 15:59:10,126:INFO: - Eval metrics : PSNR: 26.593 ; SSIM: 0.923 ; mse_loss: 158.406\n",
      "\n",
      "2018-05-30 15:59:11,163:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 15:59:11,163:INFO: Epoch 29/50\n",
      "\n",
      "2018-05-30 16:17:35,116:INFO: - Train metrics: PSNR: 26.688 ; SSIM: 0.926 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 150.568\n",
      "\n",
      "2018-05-30 16:17:40,995:INFO: - Eval metrics : PSNR: 26.592 ; SSIM: 0.923 ; mse_loss: 158.420\n",
      "\n",
      "2018-05-30 16:17:41,549:INFO: Epoch 30/50\n",
      "\n",
      "2018-05-30 16:36:06,065:INFO: - Train metrics: PSNR: 26.500 ; SSIM: 0.920 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 164.141\n",
      "\n",
      "2018-05-30 16:36:11,777:INFO: - Eval metrics : PSNR: 26.578 ; SSIM: 0.923 ; mse_loss: 158.999\n",
      "\n",
      "2018-05-30 16:36:12,357:INFO: Epoch 31/50\n",
      "\n",
      "2018-05-30 16:54:43,950:INFO: - Train metrics: PSNR: 26.214 ; SSIM: 0.918 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 176.022\n",
      "\n",
      "2018-05-30 16:54:49,841:INFO: - Eval metrics : PSNR: 26.564 ; SSIM: 0.922 ; mse_loss: 159.350\n",
      "\n",
      "2018-05-30 16:54:50,431:INFO: Epoch 32/50\n",
      "\n",
      "2018-05-30 17:14:09,539:INFO: - Train metrics: PSNR: 26.754 ; SSIM: 0.926 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 160.220\n",
      "\n",
      "2018-05-30 17:14:16,663:INFO: - Eval metrics : PSNR: 26.581 ; SSIM: 0.923 ; mse_loss: 158.819\n",
      "\n",
      "2018-05-30 17:14:17,279:INFO: Epoch 33/50\n",
      "\n",
      "2018-05-30 17:33:44,146:INFO: - Train metrics: PSNR: 26.059 ; SSIM: 0.916 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 183.364\n",
      "\n",
      "2018-05-30 17:33:51,215:INFO: - Eval metrics : PSNR: 26.587 ; SSIM: 0.922 ; mse_loss: 158.517\n",
      "\n",
      "2018-05-30 17:33:51,970:INFO: Epoch 34/50\n",
      "\n",
      "2018-05-30 17:53:17,178:INFO: - Train metrics: PSNR: 26.679 ; SSIM: 0.926 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 154.239\n",
      "\n",
      "2018-05-30 17:53:24,107:INFO: - Eval metrics : PSNR: 26.596 ; SSIM: 0.922 ; mse_loss: 158.226\n",
      "\n",
      "2018-05-30 17:53:26,109:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 17:53:26,111:INFO: Epoch 35/50\n",
      "\n",
      "2018-05-30 18:12:49,235:INFO: - Train metrics: PSNR: 26.741 ; SSIM: 0.929 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 152.905\n",
      "\n",
      "2018-05-30 18:12:55,998:INFO: - Eval metrics : PSNR: 26.616 ; SSIM: 0.923 ; mse_loss: 157.730\n",
      "\n",
      "2018-05-30 18:12:57,159:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 18:12:57,160:INFO: Epoch 36/50\n",
      "\n",
      "2018-05-30 18:32:22,862:INFO: - Train metrics: PSNR: 26.524 ; SSIM: 0.922 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 165.896\n",
      "\n",
      "2018-05-30 18:32:29,422:INFO: - Eval metrics : PSNR: 26.545 ; SSIM: 0.921 ; mse_loss: 159.712\n",
      "\n",
      "2018-05-30 18:32:30,075:INFO: Epoch 37/50\n",
      "\n",
      "2018-05-30 18:51:54,896:INFO: - Train metrics: PSNR: 26.472 ; SSIM: 0.917 ; g_loss: 0.004 ; d_loss: 1.006 ; mse_loss: 161.722\n",
      "\n",
      "2018-05-30 18:52:03,059:INFO: - Eval metrics : PSNR: 26.562 ; SSIM: 0.921 ; mse_loss: 159.364\n",
      "\n",
      "2018-05-30 18:52:03,666:INFO: Epoch 38/50\n",
      "\n",
      "2018-05-30 19:11:31,126:INFO: - Train metrics: PSNR: 26.496 ; SSIM: 0.918 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 165.034\n",
      "\n",
      "2018-05-30 19:11:38,035:INFO: - Eval metrics : PSNR: 26.582 ; SSIM: 0.922 ; mse_loss: 158.620\n",
      "\n",
      "2018-05-30 19:11:38,691:INFO: Epoch 39/50\n",
      "\n",
      "2018-05-30 19:31:05,249:INFO: - Train metrics: PSNR: 26.548 ; SSIM: 0.925 ; g_loss: 0.004 ; d_loss: 1.004 ; mse_loss: 162.282\n",
      "\n",
      "2018-05-30 19:31:11,586:INFO: - Eval metrics : PSNR: 26.582 ; SSIM: 0.923 ; mse_loss: 158.681\n",
      "\n",
      "2018-05-30 19:31:12,279:INFO: Epoch 40/50\n",
      "\n",
      "2018-05-30 19:50:38,990:INFO: - Train metrics: PSNR: 26.807 ; SSIM: 0.930 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 152.403\n",
      "\n",
      "2018-05-30 19:50:45,482:INFO: - Eval metrics : PSNR: 26.615 ; SSIM: 0.923 ; mse_loss: 157.820\n",
      "\n",
      "2018-05-30 19:50:46,162:INFO: Epoch 41/50\n",
      "\n",
      "2018-05-30 20:10:10,930:INFO: - Train metrics: PSNR: 26.838 ; SSIM: 0.929 ; g_loss: 0.004 ; d_loss: 1.018 ; mse_loss: 148.345\n",
      "\n",
      "2018-05-30 20:10:17,310:INFO: - Eval metrics : PSNR: 26.557 ; SSIM: 0.921 ; mse_loss: 159.729\n",
      "\n",
      "2018-05-30 20:10:17,950:INFO: Epoch 42/50\n",
      "\n",
      "2018-05-30 20:29:44,286:INFO: - Train metrics: PSNR: 26.932 ; SSIM: 0.930 ; g_loss: 0.004 ; d_loss: 0.998 ; mse_loss: 140.699\n",
      "\n",
      "2018-05-30 20:29:50,759:INFO: - Eval metrics : PSNR: 26.572 ; SSIM: 0.922 ; mse_loss: 159.118\n",
      "\n",
      "2018-05-30 20:29:51,350:INFO: Epoch 43/50\n",
      "\n",
      "2018-05-30 20:49:07,879:INFO: - Train metrics: PSNR: 26.502 ; SSIM: 0.925 ; g_loss: 0.004 ; d_loss: 1.001 ; mse_loss: 166.202\n",
      "\n",
      "2018-05-30 20:49:14,772:INFO: - Eval metrics : PSNR: 26.561 ; SSIM: 0.922 ; mse_loss: 159.499\n",
      "\n",
      "2018-05-30 20:49:15,505:INFO: Epoch 44/50\n",
      "\n",
      "2018-05-30 21:08:42,245:INFO: - Train metrics: PSNR: 26.534 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 1.015 ; mse_loss: 154.146\n",
      "\n",
      "2018-05-30 21:08:49,518:INFO: - Eval metrics : PSNR: 26.577 ; SSIM: 0.923 ; mse_loss: 158.967\n",
      "\n",
      "2018-05-30 21:08:50,154:INFO: Epoch 45/50\n",
      "\n",
      "2018-05-30 21:27:25,836:INFO: - Train metrics: PSNR: 26.786 ; SSIM: 0.928 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 147.795\n",
      "\n",
      "2018-05-30 21:27:31,697:INFO: - Eval metrics : PSNR: 26.586 ; SSIM: 0.922 ; mse_loss: 158.582\n",
      "\n",
      "2018-05-30 21:27:32,325:INFO: Epoch 46/50\n",
      "\n",
      "2018-05-30 21:45:56,324:INFO: - Train metrics: PSNR: 26.507 ; SSIM: 0.919 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 165.259\n",
      "\n",
      "2018-05-30 21:46:02,310:INFO: - Eval metrics : PSNR: 26.624 ; SSIM: 0.923 ; mse_loss: 157.383\n",
      "\n",
      "2018-05-30 21:46:03,412:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-30 21:46:03,412:INFO: Epoch 47/50\n",
      "\n",
      "2018-05-30 22:04:24,847:INFO: - Train metrics: PSNR: 27.095 ; SSIM: 0.930 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 145.145\n",
      "\n",
      "2018-05-30 22:04:30,842:INFO: - Eval metrics : PSNR: 26.601 ; SSIM: 0.922 ; mse_loss: 158.070\n",
      "\n",
      "2018-05-30 22:04:31,446:INFO: Epoch 48/50\n",
      "\n",
      "2018-05-30 22:22:54,802:INFO: - Train metrics: PSNR: 27.015 ; SSIM: 0.931 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 138.730\n",
      "\n",
      "2018-05-30 22:23:00,674:INFO: - Eval metrics : PSNR: 26.604 ; SSIM: 0.922 ; mse_loss: 158.132\n",
      "\n",
      "2018-05-30 22:23:01,291:INFO: Epoch 49/50\n",
      "\n",
      "2018-05-30 22:41:24,122:INFO: - Train metrics: PSNR: 26.801 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 150.495\n",
      "\n",
      "2018-05-30 22:41:30,038:INFO: - Eval metrics : PSNR: 26.616 ; SSIM: 0.922 ; mse_loss: 157.555\n",
      "\n",
      "2018-05-30 22:41:30,638:INFO: Epoch 50/50\n",
      "\n",
      "2018-05-30 22:59:54,734:INFO: - Train metrics: PSNR: 26.815 ; SSIM: 0.926 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 150.675\n",
      "\n",
      "2018-05-30 23:00:00,603:INFO: - Eval metrics : PSNR: 26.620 ; SSIM: 0.922 ; mse_loss: 157.696\n",
      "\n",
      "2018-05-31 19:16:43,536:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-31 19:16:43,564:INFO: - done.\n",
      "\n",
      "2018-05-31 19:16:49,680:INFO: Starting training for 50 epoch(s)\n",
      "\n",
      "2018-05-31 19:16:49,680:INFO: Restoring parameters from experiments/cgan_model/best_g.pth.tar\n",
      "\n",
      "2018-05-31 19:16:56,026:INFO: Epoch 1/50\n",
      "\n",
      "2018-05-31 19:19:33,927:INFO: - Train metrics: PSNR: 26.173 ; SSIM: 0.911 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 167.742\n",
      "\n",
      "2018-05-31 19:19:38,214:INFO: - Eval metrics : PSNR: 26.165 ; SSIM: 0.918 ; mse_loss: 172.979\n",
      "\n",
      "2018-05-31 19:19:39,199:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-31 19:19:39,199:INFO: Epoch 2/50\n",
      "\n",
      "2018-05-31 19:22:17,081:INFO: - Train metrics: PSNR: 26.159 ; SSIM: 0.927 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 174.503\n",
      "\n",
      "2018-05-31 19:22:21,396:INFO: - Eval metrics : PSNR: 26.163 ; SSIM: 0.918 ; mse_loss: 173.017\n",
      "\n",
      "2018-05-31 19:22:21,861:INFO: Epoch 3/50\n",
      "\n",
      "2018-05-31 19:24:59,805:INFO: - Train metrics: PSNR: 26.253 ; SSIM: 0.924 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 163.788\n",
      "\n",
      "2018-05-31 19:25:04,195:INFO: - Eval metrics : PSNR: 26.173 ; SSIM: 0.917 ; mse_loss: 172.760\n",
      "\n",
      "2018-05-31 19:25:05,053:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-31 19:25:05,054:INFO: Epoch 4/50\n",
      "\n",
      "2018-05-31 19:27:42,968:INFO: - Train metrics: PSNR: 25.620 ; SSIM: 0.912 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 186.631\n",
      "\n",
      "2018-05-31 19:27:47,296:INFO: - Eval metrics : PSNR: 26.178 ; SSIM: 0.918 ; mse_loss: 172.561\n",
      "\n",
      "2018-05-31 19:27:48,285:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-31 19:27:48,286:INFO: Epoch 5/50\n",
      "\n",
      "2018-05-31 19:30:26,226:INFO: - Train metrics: PSNR: 27.466 ; SSIM: 0.934 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 127.366\n",
      "\n",
      "2018-05-31 19:30:30,650:INFO: - Eval metrics : PSNR: 26.177 ; SSIM: 0.918 ; mse_loss: 172.530\n",
      "\n",
      "2018-05-31 19:30:31,067:INFO: Epoch 6/50\n",
      "\n",
      "2018-05-31 19:33:08,997:INFO: - Train metrics: PSNR: 26.909 ; SSIM: 0.918 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 152.322\n",
      "\n",
      "2018-05-31 19:33:13,444:INFO: - Eval metrics : PSNR: 26.172 ; SSIM: 0.917 ; mse_loss: 172.816\n",
      "\n",
      "2018-05-31 19:33:13,864:INFO: Epoch 7/50\n",
      "\n",
      "2018-05-31 19:35:51,806:INFO: - Train metrics: PSNR: 26.574 ; SSIM: 0.911 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 159.973\n",
      "\n",
      "2018-05-31 19:35:56,218:INFO: - Eval metrics : PSNR: 26.176 ; SSIM: 0.918 ; mse_loss: 172.592\n",
      "\n",
      "2018-05-31 19:35:56,675:INFO: Epoch 8/50\n",
      "\n",
      "2018-05-31 19:38:34,558:INFO: - Train metrics: PSNR: 26.416 ; SSIM: 0.917 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 165.842\n",
      "\n",
      "2018-05-31 19:38:38,889:INFO: - Eval metrics : PSNR: 26.161 ; SSIM: 0.918 ; mse_loss: 173.018\n",
      "\n",
      "2018-05-31 19:38:39,349:INFO: Epoch 9/50\n",
      "\n",
      "2018-05-31 19:41:17,339:INFO: - Train metrics: PSNR: 26.650 ; SSIM: 0.928 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 154.244\n",
      "\n",
      "2018-05-31 19:41:21,706:INFO: - Eval metrics : PSNR: 26.166 ; SSIM: 0.918 ; mse_loss: 172.933\n",
      "\n",
      "2018-05-31 19:41:22,124:INFO: Epoch 10/50\n",
      "\n",
      "2018-05-31 19:44:00,081:INFO: - Train metrics: PSNR: 26.684 ; SSIM: 0.929 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 158.209\n",
      "\n",
      "2018-05-31 19:44:04,401:INFO: - Eval metrics : PSNR: 26.161 ; SSIM: 0.918 ; mse_loss: 173.045\n",
      "\n",
      "2018-05-31 19:44:04,836:INFO: Epoch 11/50\n",
      "\n",
      "2018-05-31 19:46:42,726:INFO: - Train metrics: PSNR: 26.752 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 148.338\n",
      "\n",
      "2018-05-31 19:46:47,058:INFO: - Eval metrics : PSNR: 26.150 ; SSIM: 0.918 ; mse_loss: 173.543\n",
      "\n",
      "2018-05-31 19:46:47,442:INFO: Epoch 12/50\n",
      "\n",
      "2018-05-31 19:49:25,387:INFO: - Train metrics: PSNR: 26.696 ; SSIM: 0.918 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 158.880\n",
      "\n",
      "2018-05-31 19:49:29,728:INFO: - Eval metrics : PSNR: 26.148 ; SSIM: 0.918 ; mse_loss: 173.526\n",
      "\n",
      "2018-05-31 19:49:30,201:INFO: Epoch 13/50\n",
      "\n",
      "2018-05-31 19:52:08,109:INFO: - Train metrics: PSNR: 25.995 ; SSIM: 0.914 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 179.310\n",
      "\n",
      "2018-05-31 19:52:12,421:INFO: - Eval metrics : PSNR: 26.138 ; SSIM: 0.918 ; mse_loss: 173.984\n",
      "\n",
      "2018-05-31 19:52:12,812:INFO: Epoch 14/50\n",
      "\n",
      "2018-05-31 19:54:50,775:INFO: - Train metrics: PSNR: 26.574 ; SSIM: 0.910 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 168.444\n",
      "\n",
      "2018-05-31 19:54:55,154:INFO: - Eval metrics : PSNR: 26.116 ; SSIM: 0.916 ; mse_loss: 174.811\n",
      "\n",
      "2018-05-31 19:54:55,517:INFO: Epoch 15/50\n",
      "\n",
      "2018-05-31 19:57:33,473:INFO: - Train metrics: PSNR: 26.027 ; SSIM: 0.925 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 174.545\n",
      "\n",
      "2018-05-31 19:57:37,878:INFO: - Eval metrics : PSNR: 26.154 ; SSIM: 0.918 ; mse_loss: 173.476\n",
      "\n",
      "2018-05-31 19:57:38,353:INFO: Epoch 16/50\n",
      "\n",
      "2018-05-31 20:00:16,379:INFO: - Train metrics: PSNR: 26.523 ; SSIM: 0.920 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 155.084\n",
      "\n",
      "2018-05-31 20:00:20,746:INFO: - Eval metrics : PSNR: 26.140 ; SSIM: 0.918 ; mse_loss: 173.945\n",
      "\n",
      "2018-05-31 20:00:21,209:INFO: Epoch 17/50\n",
      "\n",
      "2018-05-31 20:02:59,200:INFO: - Train metrics: PSNR: 27.072 ; SSIM: 0.923 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 136.257\n",
      "\n",
      "2018-05-31 20:03:03,530:INFO: - Eval metrics : PSNR: 26.131 ; SSIM: 0.917 ; mse_loss: 174.218\n",
      "\n",
      "2018-05-31 20:03:03,986:INFO: Epoch 18/50\n",
      "\n",
      "2018-05-31 20:05:41,917:INFO: - Train metrics: PSNR: 27.242 ; SSIM: 0.936 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 134.509\n",
      "\n",
      "2018-05-31 20:05:46,322:INFO: - Eval metrics : PSNR: 26.150 ; SSIM: 0.918 ; mse_loss: 173.649\n",
      "\n",
      "2018-05-31 20:05:46,781:INFO: Epoch 19/50\n",
      "\n",
      "2018-05-31 20:08:24,744:INFO: - Train metrics: PSNR: 25.619 ; SSIM: 0.917 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 210.758\n",
      "\n",
      "2018-05-31 20:08:29,062:INFO: - Eval metrics : PSNR: 26.137 ; SSIM: 0.917 ; mse_loss: 173.972\n",
      "\n",
      "2018-05-31 20:08:29,523:INFO: Epoch 20/50\n",
      "\n",
      "2018-05-31 20:11:07,512:INFO: - Train metrics: PSNR: 26.361 ; SSIM: 0.920 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 161.017\n",
      "\n",
      "2018-05-31 20:11:11,817:INFO: - Eval metrics : PSNR: 26.135 ; SSIM: 0.918 ; mse_loss: 174.230\n",
      "\n",
      "2018-05-31 20:11:12,278:INFO: Epoch 21/50\n",
      "\n",
      "2018-05-31 20:13:50,273:INFO: - Train metrics: PSNR: 26.870 ; SSIM: 0.923 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 152.875\n",
      "\n",
      "2018-05-31 20:13:54,565:INFO: - Eval metrics : PSNR: 26.115 ; SSIM: 0.917 ; mse_loss: 174.895\n",
      "\n",
      "2018-05-31 20:13:55,034:INFO: Epoch 22/50\n",
      "\n",
      "2018-05-31 20:16:33,079:INFO: - Train metrics: PSNR: 27.313 ; SSIM: 0.929 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 131.591\n",
      "\n",
      "2018-05-31 20:16:37,414:INFO: - Eval metrics : PSNR: 26.125 ; SSIM: 0.917 ; mse_loss: 174.466\n",
      "\n",
      "2018-05-31 20:16:37,838:INFO: Epoch 23/50\n",
      "\n",
      "2018-05-31 20:19:15,847:INFO: - Train metrics: PSNR: 25.769 ; SSIM: 0.905 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 201.898\n",
      "\n",
      "2018-05-31 20:19:20,149:INFO: - Eval metrics : PSNR: 26.129 ; SSIM: 0.917 ; mse_loss: 174.397\n",
      "\n",
      "2018-05-31 20:19:20,616:INFO: Epoch 24/50\n",
      "\n",
      "2018-05-31 20:21:58,633:INFO: - Train metrics: PSNR: 26.441 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 157.915\n",
      "\n",
      "2018-05-31 20:22:03,133:INFO: - Eval metrics : PSNR: 26.113 ; SSIM: 0.917 ; mse_loss: 174.844\n",
      "\n",
      "2018-05-31 20:22:03,590:INFO: Epoch 25/50\n",
      "\n",
      "2018-05-31 20:24:41,596:INFO: - Train metrics: PSNR: 26.839 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 160.891\n",
      "\n",
      "2018-05-31 20:24:46,052:INFO: - Eval metrics : PSNR: 26.114 ; SSIM: 0.917 ; mse_loss: 174.979\n",
      "\n",
      "2018-05-31 20:24:46,571:INFO: Epoch 26/50\n",
      "\n",
      "2018-05-31 20:27:24,596:INFO: - Train metrics: PSNR: 26.900 ; SSIM: 0.928 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 142.608\n",
      "\n",
      "2018-05-31 20:27:28,939:INFO: - Eval metrics : PSNR: 26.116 ; SSIM: 0.917 ; mse_loss: 174.888\n",
      "\n",
      "2018-05-31 20:27:29,351:INFO: Epoch 27/50\n",
      "\n",
      "2018-05-31 20:30:07,304:INFO: - Train metrics: PSNR: 25.652 ; SSIM: 0.909 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 212.168\n",
      "\n",
      "2018-05-31 20:30:11,746:INFO: - Eval metrics : PSNR: 26.114 ; SSIM: 0.918 ; mse_loss: 174.788\n",
      "\n",
      "2018-05-31 20:30:12,201:INFO: Epoch 28/50\n",
      "\n",
      "2018-05-31 20:32:50,127:INFO: - Train metrics: PSNR: 26.723 ; SSIM: 0.929 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 157.193\n",
      "\n",
      "2018-05-31 20:32:54,467:INFO: - Eval metrics : PSNR: 26.111 ; SSIM: 0.917 ; mse_loss: 174.872\n",
      "\n",
      "2018-05-31 20:32:54,883:INFO: Epoch 29/50\n",
      "\n",
      "2018-05-31 20:35:32,852:INFO: - Train metrics: PSNR: 27.296 ; SSIM: 0.933 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 127.611\n",
      "\n",
      "2018-05-31 20:35:37,263:INFO: - Eval metrics : PSNR: 26.112 ; SSIM: 0.917 ; mse_loss: 175.012\n",
      "\n",
      "2018-05-31 20:35:37,721:INFO: Epoch 30/50\n",
      "\n",
      "2018-05-31 20:38:15,709:INFO: - Train metrics: PSNR: 25.564 ; SSIM: 0.910 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 191.654\n",
      "\n",
      "2018-05-31 20:38:20,001:INFO: - Eval metrics : PSNR: 26.103 ; SSIM: 0.917 ; mse_loss: 175.371\n",
      "\n",
      "2018-05-31 20:38:20,461:INFO: Epoch 31/50\n",
      "\n",
      "2018-05-31 20:40:58,415:INFO: - Train metrics: PSNR: 26.621 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 149.154\n",
      "\n",
      "2018-05-31 20:41:02,863:INFO: - Eval metrics : PSNR: 26.111 ; SSIM: 0.917 ; mse_loss: 175.121\n",
      "\n",
      "2018-05-31 20:41:03,282:INFO: Epoch 32/50\n",
      "\n",
      "2018-05-31 20:43:41,283:INFO: - Train metrics: PSNR: 26.717 ; SSIM: 0.936 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 161.263\n",
      "\n",
      "2018-05-31 20:43:45,576:INFO: - Eval metrics : PSNR: 26.109 ; SSIM: 0.917 ; mse_loss: 175.138\n",
      "\n",
      "2018-05-31 20:43:46,039:INFO: Epoch 33/50\n",
      "\n",
      "2018-05-31 20:46:24,057:INFO: - Train metrics: PSNR: 26.775 ; SSIM: 0.923 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 149.259\n",
      "\n",
      "2018-05-31 20:46:28,408:INFO: - Eval metrics : PSNR: 26.100 ; SSIM: 0.917 ; mse_loss: 175.463\n",
      "\n",
      "2018-05-31 20:46:28,826:INFO: Epoch 34/50\n",
      "\n",
      "2018-05-31 20:49:06,843:INFO: - Train metrics: PSNR: 27.398 ; SSIM: 0.931 ; g_loss: 0.003 ; d_loss: 1.000 ; mse_loss: 132.326\n",
      "\n",
      "2018-05-31 20:49:11,241:INFO: - Eval metrics : PSNR: 26.101 ; SSIM: 0.917 ; mse_loss: 175.416\n",
      "\n",
      "2018-05-31 20:49:11,701:INFO: Epoch 35/50\n",
      "\n",
      "2018-05-31 20:51:49,662:INFO: - Train metrics: PSNR: 26.828 ; SSIM: 0.926 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 146.242\n",
      "\n",
      "2018-05-31 20:51:54,020:INFO: - Eval metrics : PSNR: 26.091 ; SSIM: 0.917 ; mse_loss: 175.877\n",
      "\n",
      "2018-05-31 20:51:54,479:INFO: Epoch 36/50\n",
      "\n",
      "2018-05-31 20:54:32,468:INFO: - Train metrics: PSNR: 26.383 ; SSIM: 0.916 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 164.071\n",
      "\n",
      "2018-05-31 20:54:36,816:INFO: - Eval metrics : PSNR: 26.095 ; SSIM: 0.917 ; mse_loss: 175.618\n",
      "\n",
      "2018-05-31 20:54:37,273:INFO: Epoch 37/50\n",
      "\n",
      "2018-05-31 20:57:15,255:INFO: - Train metrics: PSNR: 26.677 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 153.681\n",
      "\n",
      "2018-05-31 20:57:19,614:INFO: - Eval metrics : PSNR: 26.088 ; SSIM: 0.917 ; mse_loss: 175.915\n",
      "\n",
      "2018-05-31 20:57:20,073:INFO: Epoch 38/50\n",
      "\n",
      "2018-05-31 20:59:58,153:INFO: - Train metrics: PSNR: 27.053 ; SSIM: 0.923 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 134.517\n",
      "\n",
      "2018-05-31 21:00:02,499:INFO: - Eval metrics : PSNR: 26.108 ; SSIM: 0.918 ; mse_loss: 175.117\n",
      "\n",
      "2018-05-31 21:00:02,886:INFO: Epoch 39/50\n",
      "\n",
      "2018-05-31 21:02:40,833:INFO: - Train metrics: PSNR: 26.791 ; SSIM: 0.928 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 150.549\n",
      "\n",
      "2018-05-31 21:02:45,196:INFO: - Eval metrics : PSNR: 26.084 ; SSIM: 0.917 ; mse_loss: 175.962\n",
      "\n",
      "2018-05-31 21:02:45,658:INFO: Epoch 40/50\n",
      "\n",
      "2018-05-31 21:05:23,637:INFO: - Train metrics: PSNR: 26.644 ; SSIM: 0.921 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 146.846\n",
      "\n",
      "2018-05-31 21:05:27,975:INFO: - Eval metrics : PSNR: 26.083 ; SSIM: 0.917 ; mse_loss: 176.093\n",
      "\n",
      "2018-05-31 21:05:28,368:INFO: Epoch 41/50\n",
      "\n",
      "2018-05-31 21:08:06,279:INFO: - Train metrics: PSNR: 26.802 ; SSIM: 0.919 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 150.944\n",
      "\n",
      "2018-05-31 21:08:10,623:INFO: - Eval metrics : PSNR: 26.096 ; SSIM: 0.917 ; mse_loss: 175.524\n",
      "\n",
      "2018-05-31 21:08:11,009:INFO: Epoch 42/50\n",
      "\n",
      "2018-05-31 21:10:49,041:INFO: - Train metrics: PSNR: 26.940 ; SSIM: 0.931 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 138.167\n",
      "\n",
      "2018-05-31 21:10:53,514:INFO: - Eval metrics : PSNR: 26.064 ; SSIM: 0.917 ; mse_loss: 176.639\n",
      "\n",
      "2018-05-31 21:10:53,971:INFO: Epoch 43/50\n",
      "\n",
      "2018-05-31 21:13:31,942:INFO: - Train metrics: PSNR: 25.868 ; SSIM: 0.907 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 195.177\n",
      "\n",
      "2018-05-31 21:13:36,415:INFO: - Eval metrics : PSNR: 26.075 ; SSIM: 0.917 ; mse_loss: 176.351\n",
      "\n",
      "2018-05-31 21:13:36,875:INFO: Epoch 44/50\n",
      "\n",
      "2018-05-31 21:16:14,868:INFO: - Train metrics: PSNR: 26.545 ; SSIM: 0.923 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 158.529\n",
      "\n",
      "2018-05-31 21:16:19,271:INFO: - Eval metrics : PSNR: 26.068 ; SSIM: 0.917 ; mse_loss: 176.621\n",
      "\n",
      "2018-05-31 21:16:19,670:INFO: Epoch 45/50\n",
      "\n",
      "2018-05-31 21:18:57,667:INFO: - Train metrics: PSNR: 26.880 ; SSIM: 0.933 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 150.066\n",
      "\n",
      "2018-05-31 21:19:02,015:INFO: - Eval metrics : PSNR: 26.068 ; SSIM: 0.917 ; mse_loss: 176.679\n",
      "\n",
      "2018-05-31 21:19:02,469:INFO: Epoch 46/50\n",
      "\n",
      "2018-05-31 21:21:40,375:INFO: - Train metrics: PSNR: 26.450 ; SSIM: 0.927 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 162.529\n",
      "\n",
      "2018-05-31 21:21:44,745:INFO: - Eval metrics : PSNR: 26.066 ; SSIM: 0.917 ; mse_loss: 176.739\n",
      "\n",
      "2018-05-31 21:21:45,203:INFO: Epoch 47/50\n",
      "\n",
      "2018-05-31 21:24:23,168:INFO: - Train metrics: PSNR: 26.786 ; SSIM: 0.932 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 147.483\n",
      "\n",
      "2018-05-31 21:24:27,519:INFO: - Eval metrics : PSNR: 26.081 ; SSIM: 0.917 ; mse_loss: 176.072\n",
      "\n",
      "2018-05-31 21:24:27,987:INFO: Epoch 48/50\n",
      "\n",
      "2018-05-31 21:27:05,982:INFO: - Train metrics: PSNR: 26.897 ; SSIM: 0.929 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 135.679\n",
      "\n",
      "2018-05-31 21:27:10,361:INFO: - Eval metrics : PSNR: 26.061 ; SSIM: 0.917 ; mse_loss: 176.773\n",
      "\n",
      "2018-05-31 21:27:10,759:INFO: Epoch 49/50\n",
      "\n",
      "2018-05-31 22:23:33,459:INFO: Loading the datasets...\n",
      "\n",
      "2018-05-31 22:23:33,486:INFO: - done.\n",
      "\n",
      "2018-05-31 22:23:39,637:INFO: Starting training for 50 epoch(s)\n",
      "\n",
      "2018-05-31 22:23:39,637:INFO: Restoring parameters from experiments/cgan_model/best_g.pth.tar\n",
      "\n",
      "2018-05-31 22:23:39,780:INFO: Epoch 1/50\n",
      "\n",
      "2018-05-31 22:26:17,744:INFO: - Train metrics: PSNR: 27.032 ; SSIM: 0.931 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 141.143\n",
      "\n",
      "2018-05-31 22:26:21,996:INFO: - Eval metrics : PSNR: 26.173 ; SSIM: 0.918 ; mse_loss: 172.707\n",
      "\n",
      "2018-05-31 22:26:22,962:INFO: - Found new best accuracy\n",
      "\n",
      "2018-05-31 22:26:22,963:INFO: Epoch 2/50\n",
      "\n",
      "2018-05-31 22:29:00,898:INFO: - Train metrics: PSNR: 26.202 ; SSIM: 0.928 ; g_loss: 0.005 ; d_loss: 1.000 ; mse_loss: 172.872\n",
      "\n",
      "2018-05-31 22:29:05,358:INFO: - Eval metrics : PSNR: 26.163 ; SSIM: 0.918 ; mse_loss: 172.981\n",
      "\n",
      "2018-05-31 22:29:05,762:INFO: Epoch 3/50\n",
      "\n",
      "2018-05-31 22:31:43,776:INFO: - Train metrics: PSNR: 26.295 ; SSIM: 0.924 ; g_loss: 0.004 ; d_loss: 1.000 ; mse_loss: 162.164\n",
      "\n",
      "2018-05-31 22:31:48,059:INFO: - Eval metrics : PSNR: 26.171 ; SSIM: 0.917 ; mse_loss: 172.822\n",
      "\n",
      "2018-05-31 22:31:48,523:INFO: Epoch 4/50\n",
      "\n",
      "[]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca480fd940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "\n",
    "\n",
    "def plot_loss(file_name):\n",
    "    train_loss = []   # This is actually psnr\n",
    "    val_loss = []     # This is actually loss\n",
    "    train_loss_G = []\n",
    "    train_loss_D = []\n",
    "    counter = 0\n",
    "    with open(file_name) as txtfile:\n",
    "        for row in txtfile:\n",
    "            if counter < 365:\n",
    "                counter += 1\n",
    "                continue\n",
    "            curt_row = row.split(' ')\n",
    "            if len(curt_row) >= 0 : \n",
    "#                 print (curt_row[-5])\n",
    "                if curt_row[3] == 'Train' and curt_row[-5] == 'g_loss:':\n",
    "                    print(curt_row[-4])\n",
    "                    train_loss_G.append(float(curt_row[-4]))\n",
    "                    print(curt_row[15])\n",
    "                    train_loss_D.append(float(curt_row[15]))\n",
    "\n",
    "\n",
    "\n",
    "#                 if curt_row[3] == 'Eval' and curt_row[-5] == 'g_loss:':\n",
    "#                     print(curt_row[-5])\n",
    "#                     val_loss.append(float(curt_row[-5][:-2]))\n",
    "                    \n",
    "    print (train_loss)\n",
    "    train_loss = train_loss[2:]\n",
    "    train_loss_D = train_loss_D[:20]\n",
    "    train_loss_new_D = []\n",
    "    for i in range(len((train_loss_D)) - 1):\n",
    "        train_loss_new_D.append(train_loss_D[i])\n",
    "        train_loss_new_D.append((0.75 * train_loss_D[i] + 0.25 * train_loss_D[i+1]))\n",
    "        train_loss_new_D.append((train_loss_D[i] + train_loss_D[i+1]) / 2)\n",
    "        train_loss_new_D.append((0.25 * train_loss_D[i] + 0.75 * train_loss_D[i+1]))\n",
    "\n",
    "#     val_loss = val_loss[:]\n",
    "    plt.plot(train_loss_G)\n",
    "    plt.plot(train_loss_new_D)\n",
    "\n",
    "\n",
    "cgan = './cgan_model/train.log'\n",
    "gan = './gan_model/train.log'\n",
    "gan_mse = './gan_mse_model/train.log'\n",
    "gan_ssim = './gan_ssim_model/train.log'\n",
    "gan_notv = './gan_notv_model/train.log'\n",
    "\n",
    "\n",
    "plot_loss(cgan)\n",
    "# plot_loss(gan)\n",
    "# plot_loss(gan_mse)\n",
    "# plot_loss(gan_ssim)\n",
    "# plot_loss(gan_notv)\n",
    "\n",
    "\n",
    "# plt.plot(val_loss)\n",
    "plt.legend(['train_loss_G','train_loss_D'])\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.ylabel('Loss(MSE)')\n",
    "plt.title('Loss vs Num of Epochs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.979\n",
      "\n",
      "0.960\n",
      "\n",
      "0.895\n",
      "\n",
      "0.901\n",
      "\n",
      "0.835\n",
      "\n",
      "0.987\n",
      "\n",
      "0.860\n",
      "\n",
      "0.868\n",
      "\n",
      "0.902\n",
      "\n",
      "0.850\n",
      "\n",
      "0.754\n",
      "\n",
      "0.671\n",
      "\n",
      "0.609\n",
      "\n",
      "0.528\n",
      "\n",
      "0.945\n",
      "\n",
      "0.722\n",
      "\n",
      "0.425\n",
      "\n",
      "0.806\n",
      "\n",
      "0.860\n",
      "\n",
      "0.581\n",
      "\n",
      "0.359\n",
      "\n",
      "0.507\n",
      "\n",
      "0.291\n",
      "\n",
      "0.323\n",
      "\n",
      "0.408\n",
      "\n",
      "0.354\n",
      "\n",
      "1.149\n",
      "\n",
      "0.969\n",
      "\n",
      "0.670\n",
      "\n",
      "0.850\n",
      "\n",
      "0.902\n",
      "\n",
      "0.576\n",
      "\n",
      "0.407\n",
      "\n",
      "0.936\n",
      "\n",
      "0.492\n",
      "\n",
      "0.329\n",
      "\n",
      "0.913\n",
      "\n",
      "0.611\n",
      "\n",
      "0.337\n",
      "\n",
      "0.872\n",
      "\n",
      "1.144\n",
      "\n",
      "0.995\n",
      "\n",
      "1.025\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.001\n",
      "\n",
      "1.001\n",
      "\n",
      "1.001\n",
      "\n",
      "1.001\n",
      "\n",
      "1.001\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "0.999\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "1.000\n",
      "\n",
      "[0.979, 0.96, 0.895, 0.901, 0.835, 0.987, 0.86, 0.868, 0.902, 0.85, 0.754, 0.671, 0.609, 0.528, 0.945, 0.722, 0.425, 0.806, 0.86, 0.581, 0.359, 0.507, 0.291, 0.323, 0.408, 0.354, 1.149, 0.969, 0.67, 0.85, 0.902, 0.576, 0.407, 0.936, 0.492, 0.329, 0.913, 0.611, 0.337, 0.872, 1.144, 0.995, 1.025, 1.0, 1.0, 1.0, 1.0, 1.001, 1.001, 1.001, 1.001, 1.001, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.999, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f01bc829128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_loss(file_name):\n",
    "    train_loss = []   # This is actually psnr\n",
    "    val_loss = []     # This is actually loss\n",
    "    counter = 0\n",
    "    with open(file_name) as txtfile:\n",
    "        for row in txtfile:\n",
    "            if counter < 365:\n",
    "                counter += 1\n",
    "                continue\n",
    "            curt_row = row.split(' ')\n",
    "            if len(curt_row) >= 5 : \n",
    "#                 print (curt_row[-2])\n",
    "                if curt_row[3] == 'Train' and curt_row[-2] == 'd_loss:':\n",
    "                    print(curt_row[-1])\n",
    "                    train_loss.append(float(curt_row[-1]))\n",
    "#                 if curt_row[3] == 'Eval' and curt_row[-5] == 'g_loss:':\n",
    "#                     print(curt_row[-5])\n",
    "#                     val_loss.append(float(curt_row[-5][:-2]))\n",
    "                    \n",
    "    print (train_loss)\n",
    "    train_loss = train_loss[2:]\n",
    "#     val_loss = val_loss[:]\n",
    "    plt.plot(train_loss)\n",
    "#     plt.plot(val_loss)\n",
    "\n",
    "\n",
    "cgan = './cgan_model/train.log'\n",
    "gan = './gan_model/train.log'\n",
    "gan_mse = './gan_mse_model/train.log'\n",
    "gan_ssim = './gan_ssim_model/train.log'\n",
    "gan_notv = './gan_notv_model/train.log'\n",
    "\n",
    "\n",
    "plot_loss(cgan)\n",
    "# plot_loss(gan)\n",
    "# plot_loss(gan_mse)\n",
    "# plot_loss(gan_ssim)\n",
    "# plot_loss(gan_notv)\n",
    "\n",
    "\n",
    "# plt.plot(val_loss)\n",
    "plt.legend(['GAN','GAN-MSE','GAN-SSIM','GAN-NOTV'])\n",
    "plt.xlabel('Number of epochs')\n",
    "plt.ylabel('Loss(MSE)')\n",
    "plt.title('Loss vs Num of Epochs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
